GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
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Jala Jevaani
PARTICIPATORY GEO-GRAPHICAL MANAGEMENT INFORMATION SYSTEM OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME Vision without action is a daydream, Action without vision is nightmare Japanese proverb
Anne Chappuis September 2003
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
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Contents
1. THE DEFINITION AND IMPORTANCE OF THE PARTICIPATORY GEO-GRAPHICAL MANAGEMENT INFORMATION SYSTEM ................................................................................................... 4 1.1. 1.1.1. 1.2. 1.2.1. 1.2.2. 1.2.3. 1.2.4. 1.3. 1.3.1. 1.3.2. 1.3.3. 1.3.4. 1.3.5. 1.3.6.
AN INTEGRATED APPROACH .................................................................................................................. 5 GEOPHYSICAL INFORMATION ........................................................................................................... 5 OBJECTIVES OF A P.G.M.I.S. ................................................................................................................. 6 PLANNING ......................................................................................................................................... 6 MONITORING ..................................................................................................................................... 6 TECHNICAL HELP .............................................................................................................................. 6 COMMUNICATION AND SELF IMPROVEMENT OF THE SYSTEM ........................................................ 6 WHAT ARE THE TECHNICAL PREREQUISITES ...................................................................................... 6 GEOREFERENCING ............................................................................................................................ 6 NOTIONAL ADMINISTRATIVE BOUNDARIES ARE ACCEPTABLE ....................................................... 6 SPECIAL CARE ABOUT CODIFICATION .............................................................................................. 6 SPECIAL CARE WHEN DIGITISING GEOGRAPHICAL DATA ................................................................ 7 A PROPER METHODOLOGY TO STRUCTURE THE SYSTEM ................................................................ 7 A PROPER METHODOLOGY TO VISUALISE DATA.............................................................................. 7
2.
OBJECTIVES OF THE WATERSHED DEVELOPMENT PROGRAMME ....................................... 8
3.
DEFINING AND USING INDICATORS ................................................................................................... 9
3.1. 3.2. 3.2.1. 3.2.2. 3.2.3. 3.2.4. 3.2.5. 3.2.6. 3.2.7. 4.
THE VILLAGE INFORMATION SYSTEM (VIS) ................................................................................ 22
4.1. 4.1.1. 4.1.2. 4.1.3. 4.2. 4.3. 5. 5.1. 5.2. 5.3.
WHAT ARE INDICATORS ......................................................................................................................... 9 THE SELECTION AND USE OF INDICATORS............................................................................................ 9 ASSESSING INEQUITY ..................................................................................................................... 10 ASSESSING EDUCATION AND GENDER EQUITY ............................................................................. 11 ASSESSING EDUCATION AND SOCIAL EQUITY .............................................................................. 12 ASSESSING HEALTH ........................................................................................................................ 13 ASSESSING THE ECONOMY ............................................................................................................. 14 ASSESSING SOCIAL CAPITAL .......................................................................................................... 18 LINKING INDICATORS ..................................................................................................................... 19
PBAS DATA ........................................................................................................................................... 26 SHG QUESTIONNAIRE:.................................................................................................................... 26 UG QUESTIONNAIRE:...................................................................................................................... 26 PARTICIPATORY SITUATION ANALYSIS (PSA) POSTERS: ............................................................. 26 PHYSICAL AND FINANCIAL MONITORING DATA ............................................................................... 28 EXTERNAL DATA ................................................................................................................................... 28
USING DATA TO IDENTIFY GAPS AND CREATE ACTION PLANS ............................................ 29 ENCOURAGING ECONOMIC, GENDER AND SOCIAL EQUITY: IDENTIFYING GAPS ........................ 34 STRENGTHENING SHGS: NUMBERS, ECONOMICS AND SOCIAL COMPOSITION ............................ 36 IMPROVING EDUCATION ...................................................................................................................... 39
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
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MONITORING AND EVALUATION (M&E) ........................................................................................ 42
6.1. 6.2. 6.3. 6.4. 7.
M & E AT THE WATERSHED LEVELS .................................................................................................. 42 M & E AT THE PIA LEVEL ................................................................................................................... 44 M & E AT THE PD LEVEL..................................................................................................................... 46 M & E AT THE CRD LEVEL ................................................................................................................. 49 WATER MANAGEMENT.......................................................................................................................... 53
7.1. 7.2. 8.
AN OVERVIEW ........................................................................................................................................ 53 THE WORRYING STATE OF GROUNDWATER AND TANK IRRIGATION .............................................. 56 THE USE OF GRAPHICAL TECHNIQUES: BERTIN AND HIS CONTRIBUTION ..................... 58
8.1. 8.2. 8.3. 8.4. 8.5. 8.5.1. 8.5.2. 8.6. 8.6.1. 8.6.2. 8.6.3. 8.7. 9.
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WHY REPRESENT INFORMATION VISUALLY ....................................................................................... 58 MATHEMATICS AND GRAPHICS: TWO TYPES OF PERCEPTION ........................................................ 59 A GRAPHIC! TO DO WHAT? LEVELS OF INFORMATION .................................................................... 60 PROPERTIES OF THE IMAGE ................................................................................................................. 61 TYPES OF DATA ...................................................................................................................................... 61 QUANTITATIVE ............................................................................................................................... 61 QUALITATIVE .................................................................................................................................. 62 THE BUILDING BLOCKS OF BERTIN’S GRAPHIC LANGUAGE: VISUAL VARIABLES ......................... 63 ORDERED AND DISSOCIATIVE VARIABLES ..................................................................................... 63 SELECTIVE AND ASSOCIATIVE VARIABLES .................................................................................... 67 PURELY ASSOCIATIVE VARIABLE ................................................................................................... 69 THE VISUAL MATRIX ............................................................................................................................. 73
LIST OF MAPS ............................................................................................................................................ 78
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
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The definition and importance of the Participatory GeoGraphical Management Information System Participatory…
Building of the system should be done WITH the stakeholders, not FOR them. Graphical outputs should reach them, even if illiterates.
…Geo-Graphical… Geographical dimension varies at different levels from micro-level, watershed, where detailed maps are required, to macro-level, district and state, where notional maps are sufficient. Graphical dimension is equally important, specially in a context where stakeholders are not all literates. Graphic tables help monitor the gaps at a glance and hence would allow the Project Directors to save time and focus on the WDTs which show the more gaps.
…Management Emphasis should be more on management in the programme, both in terms of data management for monitoring and in terms of natural resource management. Remarks from « Water Resources Audit », KAWAD REPORT 17 JUNE 2000 : « Water demand for domestic and livestock purpose is likely to double in the next 30 years. There has been a dramatic increase in ground water extraction for irrigation. Competition between agricultural and domestic uses will increase. It happens already in years of low rainfall. When this happens, it is generally the poor that are hit the hardest. Runoff is low, between 2 to 6 %. So there is not much scope for water harvesting. Focus should be on water management. » The focus should be shifted from Watershed DEVELOPMENT to water MANAGEMENT.
…Information SYSTEM A systematic approach should be adopted. The quality of the system depends on the quality of inputs, a systematic database and database management has to be built. It has to start with an account system accompanied by complementary data on the watershed committee, association, WDT, socio-economic (population, crops, livestock, groups, etc.). Reports should be generated from this database only. Adhoc reports will not be necessary anymore. A data manager should be made responsible for the district database. It should have the same responsibilities as the officer in charge of filing the paper files. A watershed should be considered as a file. Nobody else can enter a file number and nobody can alter the content of a recorded file. Only amendments can be made. Validation checks should be made at the time of data entry, and further validated through calculation to bring out inconsistencies. The model which has been developed in Kurnool should be replicated in all districts, which means that all districts should computerise and train their MDTs on war footing. PDs should also be trained on the use of the database and MIS which will be derived from the database.
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
1.1.
An integrated approach
Geophysical information Soils Land use Hydrogeomorphology Drainage Slopes Transport Settlements Groundwater
Administrative information Cadastre Habitation Revenue villages Mandals Constituencies
Physical G.I.S.
Administrative G.I.S.
Geographical Information System Combination of layers
Geographical Information System Combination of layers
Creation of physical typologies ogy
Creation of administrative zones typology
Accounts (vouchers) database Watershed Comittee members / W.D.T. members Watershed socio-economic data Timely evaluation
M.IS. Management Information System
Reports 1.1.1.1.Analysis
P.G.M.IS. 1.1.1.2. Participatory Geographical Management Information System
Different queries and displays for different users : • WA & WC • P.I.A. • • •
•
M.D.T. M.L.A. / M.P. P.D.
C.R.D.
At different scales / geographical units : • Micro watershed • Macro watershed • Village • Mandal • Constituency • District
• State
With different tools : • • • • • • •
Arcinfo Erdas Arcview ArcExplorer Macmap Access Excel
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GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
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1.2. Objectives of a P.G.M.I.S.
1.2.1. Planning Integrate data from different sources for analysis, comparison of indicators to define priority areas and identify priority watersheds. The combination of geophysical indicators, socio-economic indicators and groundwater measurements will allow grading of areas into high, medium, low priority
1.2.2. Monitoring The efficacy of the projects can be assessed through impact analysis (evolution of groundwater level, rejuvenation of wells, reduction in labour migration, increase in agricultural production, afforestation, etc.), follow up of expenses by type of work (physical and community oriented), monitoring the performances of P.I.A.s and MDTs. Periodical reports can be output to follow up in time the progress of work.
1.2.3. Technical help Through combination of geophysical indicators grading of areas within the watershed can be done to help decide which type of work should be done. Combination of soil, geomorphology, slope, groundwater level can be used to define micro-areas which can be graded. In case of adjacent watersheds care can be taken to build more consistent programmes bringing an integrated approach to water basin.
1.2.4. Communication and self improvement of the system Maps and graphs are easily understable even by illiterates. Information can be sent back to the villagers who can check it and make use of it. In case of projects which require the cooperation of several villages it becomes easy to discuss in front of the map. Gaps and errors which are not visible in tables will be immediately visible on a map. This will lead to self improvement of the system.
1.3. What are the technical prerequisites
1.3.1. Georeferencing Geographical information should be brought into the same coordinate system to be able to superimpose the layers. The basis should be the Survey of India maps. All other information (satellite, administrative, etc.) should be reprojected.
1.3.2. Notional administrative boundaries are acceptable Administrative boundaries are not available on the SoI maps. They are available with the Revenue department and in the Census handbooks. But they are notional and do not match precisely the Village Survey maps. Nevertheless it is better than nothing and can be used to plot the information.
1.3.3. Special care about codification Codification is of the utmost importance and special care should be taken when building codes. They should preferably be stored in a character format rather than in a numeric format (this is a standard requirement of proper database management). Great care should be taken when inputing codes in geographical object (geocodes). For instance the habitation codes in the village
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maps given for Janmabhoomi are wrongly placed. Hence the data which will be linked through the code will be wrongly located. Updating of new codes should be done in the master code file whenever a new code is given.
1.3.4. Special care when digitising geographical data When digitising information such as watershed boundaries one should start from a district map where all the boundaries will be digitised. This will give a georeferenced frame in which future watersheds can be integrated. When this first phase is over one can separate the watersheds if necessary. If this is not done and watersheds are digitised separately from the beginning, consolidation of the waresheds will be a problem. The boundaries of two adjacent watersheds will not coincide. This is what happened to the district maps given for Janmabhoomi.
1.3.5. A proper methodology to structure the system Structuring a GMIS requires a strict methodology. It is but a geographical database, without a proper method at the time of building it, it will give problems at the time of querrying, updating, improving and ultimately it will have to be discarded and a new one will have to be built. Besides being a database it deals with geographic objects and there are specific rules for digitisation and building geographical objects. Training on the tools (G.I.S. software) is not sufficient. Training on the method is required.
1.3.6. A proper methodology to visualise data Transforming data into an effective picture has to follow certain specific rules. If not followed a map can be completely misleading. This rules have been developed by Prof. Bertin and are now recognized as basics in the US. But unfortunately they are not integrated in GIS packages yet. Training on the rules to build efficient maps is required.
PGMIS is not a magical system. Time is required. It cannot be built overnight. And it is not a press button system. It cannot be done at a fire fighting pace. But once the system is built it can help fighting fire.
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
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OBJECTIVES PROGRAMME
•
•
•
OF
THE
WATERSHED
• • •
DEVELOPMENT
GO Project Memo Process Guidelines for Watershed Development Programme To promote the economic development of the village community that is, directly or indirectly, dependent on the watershed, through: o Optimum utilisation and management of natural resources (land, water, vegetation etc.) to mitigate the adverse effects of drought and desertification, and prevent further ecological degradation o Employment generation and development of human and economic resources to promote savings and income-generating activities To restore the ecological balance of the village through: o Harnessing, conserving and developing natural resources i.e. land, water and vegetation o Sustained community action for the operation and maintenance of assets created, and further development of natural resource potential o Simple, easy and affordable technologies and institutional arrangements that make use of local knowledge and available materials To improve the economic and social conditions of the resource-poor and disadvantaged sections of the Watershed Community through: o More equitable distribution of land and water resources and biomass production o Greater access to income-generating opportunities and focus on human resource development People-related Goals
• • • • •
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Water management (drinking) Self-planning & monitoring Equity (gender, social) Poverty alleviation/livelihood Access to information (on new initiatives, etc.)
Land-based Goals • • • •
Water management (irrigation/cattle) Development of waste/degraded lands, and drought prone/desert areas Enhanced productivity Reduced susceptibility to drought
These goals can be translated into reality through: Participatory planning Effective monitoring Creating graphic outputs that can be understood by everyone, including those who are illiterate
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Defining and Using Indicators
3.1. What are indicators Indicators are characteristics that are used to describe and/or quantify, and thereby grade the object being measured. For example, when measuring health, some of the indicators used are life expectancy at birth infant mortality % of children immunized. The above three indicators would help us to create a description of a given area and to grade or classify it depending on how it compares with a similar indicator-description of other areas. It must be emphasised that an indicator does not in itself represent the basic unit of information. Indicators are entities that are derived from basic units of information. It is of vital importance not to confuse indicators with the fundamental unit of information in a database. For example, if we want to measure livelihood improvement through an increase in milk sales the indicator we are looking for is the rise in the total village milk yield. In order to obtain this indicator, however, we cannot directly ask the village community to provide us with a global village figure of the milk yield. Data must first be collected from individuals/families, through a sample survey system. Only after a database has been constituted from this information collected can we safely derive the indicator we are looking for, i.e, total increase in village milk yield. The basic unit of information, in this case individual milk yield per family, helps us to derive the indicator that we are looking for, the total amount of the village’s milk yield. Indicators do not constitute the primary elements of a database. Rather, they are constituted from databases.
3.2. The selection and use of indicators The Human Development Report (published yearly by the United Nations Development Programme) has created indices to measure aspects of human development. Examples of the indicators that are used in developing these indices are shown below: Human Development Index (HDI) Indicators Measures achievement in three basic Life expectancy at birth dimensions of human development: health, Adult literacy rate education and standard of living. GDP per capita (PPP US$) Human Poverty Index (HPI) Indicators Measures deprivation in health, education Adult illiteracy rate and standard of living. % of population not using improved water sources % of children under 5 who are underweight Gender-related Development Index (GDI) Indicators Reflects the inequalities between men and Female versus male life expectancy at birth women in health, education and standard of Female versus male adult literacy rate living. Female versus male estimated earned income Gender Empowerment Measure (GEM) Indicators Captures gender inequality in three key Female and male Parliamentary seats areas: political participation and decision- Female and male legislators, senior officials making power, economic participation and and managers decision-making power, and power over Female and male professional and technical economic resources. positions Source: Human Development Report, 2002
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For our purposes in APRLP we have classified indicators into Indicators for planning and Indicators for monitoring Thus, the indicators we choose for the planning of a project need not necessarily be same the indicators we choose for its monitoring. In each case, indicators are chosen carefully for the efficacy and accuracy of their description of a particular situation or object. To provide a concrete example the extent/quantity and status (level of degradation) of common property resources would be an appropriate indicator for planning whereas the SHG PSA chart would figure as an indicator for monitoring. Overall methodological approach for deriving and using indicators Identify needs→Basic data →Indicators→Analysis→Identify gaps→Planning To begin with, therefore, it is of vital importance to identify the issues that need to be addressed before deciding upon the indicators that will be used to quantify and analyse the issues. These issues are: 1. Gender Inequity 2. Social Inequity 3. Education 4. Health 5. Economy 6. Social Capital We shall deal with each of these items separately.
3.2.1. Assessing Inequity The term ‘equity’ refers to fairness or justice. Gender inequity refers to the differences between the situations of men and women. A proper understanding of these injustices and imbalances is needed in order to redress them. Social inequity needs to be studied in order that we may work toward a more equal society in terms of class, caste and community. Gender and social inequity can only be studied by, first, assessing the general level/state of knowledge, health, economy and social capital, and, then, seeing how these levels vary according to gender and social category. A measurement of inequity must therefore be derived from an assessment of the four other items.
Data + indicators for Education, Health, Economy and social capital
Indicators for Gender Equity
Indicators for Social Equity
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3.2.2. Assessing Education and Gender Equity • •
The following data can be used when assessing the status of education: literacy rates (census data from 1991 at the village level and 2001 at the mandal level) enrolment and dropout rates (Dept. Of Education).
Data on education can be used to study gender and social equity. Since we cannot directly measure gender equity, we have to find indirect ways of quantifying it, by using indicators. The above data can be used to derive indicators for gender inequity in access to education: 1. % female literates [no. of female literates/ female population over 6 yrs] x 100 is an indicator of women’s access to education 2. % female literates/ % male literates gives the literacy sex ratio, which is the indicator for gender equity in access to knowledge When calculating the literacy sex ratio, percentages must be used, not numbers of literate females/males. This is a particularly recurrent error and must Map 1 Gender deprivation, Mahbubnagar
This map depicts the deprivation associated with gender in access to education. It is immediately noticeable that the western part of the district has a very low literacy rate for females, as well as a very low literacy sex ratio. Using the chosen indicators the following conclusion can safely be drawn • Watersheds in the western region are characterized by gender inequity, and need action plans that will prioritise equity concerns.
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3.2.3. Assessing Education and Social Equity One of the indicators of social inequity in access to education is the dropout rate for SC/STs compared to the average dropout rate. Map 2 Dropout rate, Mahbubnagar
• • • •
The indicators selected tell us that: Andhra Pradesh has an average dropout rate of 34.5%, which indicates that access to education is poor. The dropout rate of SCs (41.3%) and STs (62.6%) is higher than the State average (34.5%) by 7 points and 28 points, respectively. Mahbubnagar has a dropout rate of 59.5%, which is 25 points higher than the state average. The dropout rates for SCs and STs in Mahbubnagar are 6 points and 21 points more than the district average respectively, and 25 points and 18 points more than the state average. Taking into account the above description
Action plans for Mahbubnagar need to address the poor state of education in this district, and concentrate on improving, in particular, the educational situation of women and SC and ST communities.
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3.2.4. Assessing Health Data sources being limited in this particular area we have to choose our indicators carefully. The general quality of health can be assessed by using available indicators such as • infant mortality • number of hospitals/1000 people, child immunization rates etc. One reason for unnatural imbalances in sex ratios could be the poor state of female nutrition and the lack of medical attention for women and girls. Thus, when assessing the health status of a population as a whole we must also look at inequities within the health care system. Map 3 Sex ratio Mahbubnagar
source: census of India 1991
• •
Dark areas are regions characterized by gender inequity Low female sex ration could be a result of a skewed child sex ratio, and/or deprivation in adulthood (poorer access to health facilities, worse nutrition etc.).
It should be noted that The percentage of female children has decreased since 1991 This is quite likely due to poor state of female nutrition and health care
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We also need to collect data on access to health facilities, and we need to map health facilities
3.2.5. Assessing the Economy See CDSA1 study ‘Design Inputs for Social and Institutional Development’, December 1998. The economy of a region can be assessed by estimating income levels through occupations listed in census data, looking at landholding patterns, cropping patterns, productivity, prices, wage rates, etc. (DES) Map 4 Agricultural labourers, Mahbubnagar
Using one such indicator, agricultural labour, we know that • Mahbubnagar has a high district rural average for agricultural labourers • The highest concentration of agricultural labourers is in the south west • A high percentage of agricultural labourers usually indicates a high percentage of resource poor, working for low wages i.e. it is an indicator of poverty
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Centre for Development Studies and Activities, Pune. This report was submitted to Rural Development Office, DFID, New Delhi.
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Map 5 Female agricultural labourers
Another indicator of the level of poverty is the percentage of working children. Map 6 Rural working children, AP
source: census of India 1991
A study of this indicator shows that • Mahbubnagar and Kurnool have the highest percentage of working children in the state. A high percentage of working children indicates poverty, and leads to low literacy rates. • In most districts, the percentage of working girls is higher than that for boys, indicating gender discrimination against female children.
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Economic data can also be used to generate indicators for social equity, e.g. if we study the % landholding by community we can easily determine those sections of the population that are less favourably placed than others.. Map 7 Marginal farmers, AP
source: information courtesy CESS
A map of this indicator shows that • Areas to the north and the coastline are dominated by marginal farmers. A high percentage of small landholdings is an indicator of poverty. We need to utilize satellite images of landuse for Wanaparthi
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Map 8 Access to banks, AP
source: census of India 1991
The inference is clear - A low number of banks/1000 people is an indicator of poverty and backwardness.
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3.2.6. Assessing Social Capital Social capital consists of both physical entities that can be developed for the common good (Common Property Resources), as well as human systems that empower a community (network of SHGs, UGs, WSA etc.). Map 9 uncultivated land AP
source: bureau of economics and statistics, AP
Land use data can be used to identify CPRs. These can be broadly divided into four categories. 1. Community Lands 2. Forest area 3. Community Wells 4. Tanks In the APRLP baseline, data is collected from both UG and SHG groups on the current status of each of the above CPRs. This data describes the no/area of CPRs available, and also describes the level of degradation as well as the potential to develop them for the common good. Using a combination of these three criteria (quantity, degradation and potential) community land is categorized into five priorities at the watershed level. When preparing action plans these priorities are linked to the number of livestock and landless farmers, and the performance of SHGs. If the availability of common lands is greater in a particular watershed, the CPR priority is higher, and depending on the fodder requirement (based on livestock numbers in that watershed) a decision can be taken as to how to use the common lands i.e. whether they should be used for fodder pasture development, dry land horticulture, or something else. Availability of common land can also be linked to the % of landless and marginal farmers in each watershed. Those watersheds that have a high percentage of landless or marginal farmers, as well as available common lands, need to be identified so that livelihood interventions are planned in order to develop CPRs and benefit the poor. In such watersheds, data on the strengths of SHGs and the presence of VOs can also be obtained from PSA posters 5 and/or 4. A decision can then be taken as to where the groups should be strengthened first, and where the VO should be formed first, so as to implement efficient plans for CPR development.
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3.2.7. Linking Indicators By constructing a district matrix of important indicators, we can link and compare indicators to highlight trends, and prioritise actions. It is only through linking indicators that we can make action plans for issues such as CPRs. In the matrix below, values that are average and below average are shown in grey, and values above average are shown in black. In the example provided here, a certain number of indicators, such as literacy sex ratio and the percentage of marginal landholdings, have been used to create a profile of each district in the state. The resulting profiles help us to classify the districts depending on their relative development. Such profiles help us create what is known as a typology of districts. It must be understood that the author has chosen only those indicators that he/she sees as indicative of the level of development.
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
Map 10 Typology of districts, AP
source: census of India 1991
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After considering a selection of indicators for the entire district, it is possible to rank the districts into categories, depending on the level of development. The districts in Andhra Pradesh have been categorized as follows: The APRLP watersheds are located in five districts: 1. Nalgonda: Backward 2 2. Mahbubnagar: Most backward 3. Kurnool: Most backward 4. Ananthapur: Backward 3 5. Prakasham: Backward 1
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4.
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THE VILLAGE INFORMATION SYSTEM (VIS)
APRLP envisages a radical new approach to watershed management: encouraging participatory and sustainable rural livelihood initiatives. Such an approach requires an interactive information management system. Data from a number of sources is combined at the level of the Habitation and at the Revenue Village to form the Village Information System (VIS). The VIS is the most important database when preparing action plans. The objectives of the GO are to: 1) Help reduce poverty 2) Help reduce vulnerability to drought The four essential components of APRLP are watershed plans, capacity building for primary and secondary stakeholders, innovation, and lesson learning. This will require new approaches in: • Rural data collection, planning, and convergence • Analysing the constraints and opportunities facing vulnerable groups • Encouraging grassroots organizations, and the development of participatory plans reflecting the needs of the rural poor The APRLP hierarchy includes both land- and people-based initiatives, and encourages a fluid flow of information between the different levels.
APRLP Land People CRD PD MDT PIA/WDT WSA/WSC - SHG/VO
This new system encourages and requires a new management approach: The APRLP programme requires a drastic change in approach to rural development. The most important aspect of the program is a bottom-up approach to the involvement of people. The VIS requires added value information. This means that any data/information collected from villagers must be given back to them, with an added value. For example, if information on literacy has been collected, the villagers should be shown how their literacy rate compares to that of the district. The VIS aims to transform rigid, top-down ‘management’ into fluid ‘facilitation’ that will enable villagers to understand their situation, identify gaps/needs, and take corrective measures by formulating their own action plans. The VIS will enable:
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
•
Local people’s planning
•
Local need-based decisions
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GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
Capacity building and empowerment
•
•
Networking and convergence
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The involvement of local people will require a change in attitude and vocabulary: • • • • • • • •
MANAGEMENT FACILITATING AGENCY STAKEHOLDERS PARTICIPATORY PLAN CAPACITY BUILDING EMPOWERMENT NETWORK CONVERGENCE
NOT
• • • • • • • •
MONITORING IMPLEMENTING AGENCY BENEFICIARIES PLAN TRAINING ASSISTANCE ISOLATED ACTIONS SEPARATE ACTIVITIES
The APRLP approach will also require management tools at the field level, to: • Assess the situation • Identify potential for development • Analyse links • Help plan activities • Help prioritise work The VIS will generate tools to be used by villagers (UG, SHG, WSA, WSC, VO). These tools will be simple, visual, and based on village culture, not urban culture. They will be in the form of: • PSA posters • Graphic Reports • Maps All information pertaining to the Revenue Village must be entered into the VIS, and all information in the VIS must be available to the villagers. The objective of these graphic tools is to make work easier: • Reports can be generated automatically from a database, saving time for the staff o Data, once collected, will be entered into the system, and can be retrieved easily by WDT/PIA o MDT/PD will save time on data compilation • Graphic outputs help o Discussions > Democracy o Understand the situation > Communication o Compare with Mandal/District > Set goals o Prepare micro-plan > Participation o Follow-up implementation > Transparency Three types of data have been used by APRLP in preparing graphic outputs: 1. PARTICIPATORY BASELINE ASSESSMENT SURVEY (PBAS) DATA 2. PHYSICAL AND FINANCIAL MONITORING DATA 3. EXTERNAL DATA
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
26
4.1. PBAS Data This data is specific to APRLP watersheds. The main objectives of the PBAS are to: 1. Understand ground realities 2. Identify key issues 3. Help in the preparation of micro-plans 4. Initiate self-monitoring systems 5. Serve as a benchmark in assessing the success of watershed programmes The PBAS is conducted through SHG and UG questionnaires, and PSA posters. The types of information collected are:
4.1.1. SHG questionnaire: • • • • • • • • • • •
Available CPRs Livestock development Drinking water and sanitation Migration Poorest households Sanghamitrulu and leadership Health, nutrition and education Problems/positive issues in relation to APRLP watersheds SHG status Livelihood analysis Functioning of Watershed Committee
4.1.2. UG questionnaire: • • • • • • • • • • • • •
Available CPRs Livestock development Landholding by farmer category WDF: mode of collection; use; maintenance of structures Migration Coping mechanisms in case of drought, disablement, death of earning member Equity and poor Social regulation on use of natural resources Productivity of principal crops Irrigation sources/area Problems/positive issues in context of APRLP watersheds Data on ABC categorization Data on QPR Socio-economic questions have to be asked only at the habitation level
4.1.3. Participatory Situation Analysis (PSA) posters: • • • •
The main themes of the PSA posters are Functioning of SHGs Functioning of WSCs Status and scope of livelihoods Water resources
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
27
There are seven posters for self-monitoring by the community: Poster I: Functioning of Watershed Committee Poster II: Agro- and Livestock-based Livelihoods Poster III: Off-farm Livelihoods and Information about Shandies / Markets. Poster IV: Consolidated status of Self Help Groups and the Role of Women in village level institutions. Poster V: Status of individual Self Help Groups. Poster VI: Water Resources (drinking water) Poster VII: Water Resources (irrigation) 4.1.3.1.POSTER 1 This poster deals with the functioning of the Watershed Committee. Fifteen indicators have been selected for this purpose, and are grouped into four categories based on their functional relationship, as shown below: List of Category
List of the Indicator 1. Regularity of meetings 2. Attendance of meetings 3. Involvement of User Groups
Transparency
4. Preparation of action plan 5. Discussion on Proposals 6. Works allocation 7. Transparency and Accountability 1. Monitoring of works
Implementation
2. Works Payment 3. Record Maintenance
Post-Project Sustainability
1. Maintenance of Structures 2. Collection of WDF 1. Training
Institutional & Livelihood Aspects
2. Livelihood Intervention 3. Women’s involvement in Watershed Committee
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28
Performance of Watershed Committees
The above graph can be used to measure the performance of watershed committees at the PIA/MDT/PD levels. Percentages are calculated according to the total number of watersheds being considered. The performance for each indicator is divided into four categories: very poor, poor, good and very good. • If the percentage of watersheds within a performance category exceeds 50%, it is shown in blue (for high and very high performance) and red (for low and very low performance). • Percentages that exceed the 50% mark in the High and Very High categories are a good sign, while percentages that cross the 50% mark in the Low and Very Low categories are a bad sign. • Watershed Committees are considered to be performing well when the majority of indicators exceed 50% in the Good and Very Good categories. • If the majority of indicators exceed 50% in the Poor and Very Poor categories, the performance of WSCs is considered poor.
4.2. Physical and Financial Monitoring Data This data is taken from the Quarterly Progress Reports (QPRs). QPRs contain cumulative details on the physical, financial and social aspects of all the watersheds in a district. They follow a basic reporting format that shows the progress of watersheds in all the districts.
4.3. External Data This consists of general data from all the watersheds, and data from different government and non-governmental sources.
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5.
USING DATA TO IDENTIFY GAPS AND CREATE ACTION PLANS
When creating action plans, the same process is followed as outlined in the beginning of this document: 1. Data is collected 2. Indicators are used to analyse the data, and identify gaps 3. Actions are prioritised according to the level of need 4. Once priorities have been set, the Action Plan is formulated at the watershed level. A sample village action plan for Thippayapalli is shown below: VILLAGE ACTION PLAN Village: Thippayapalli
STATUS • • • • •
• •
Common land = 500 acres Livestock population = 1630 cow units Low livestock pressure = 3.3 cow units/acre Trend = increasing Fodder requirements: Dry fodder = 8 tons/day Green fodder = 11 tons/day Landless families = 50 Common land/landless family = 10 acres
PIA: Assist
THEME
CPR
LIVESTOCK POPULATION
LANDLESS FAMILIES
ACTION • Prioritise development of 500 acres of common land • Plan fodder development through SHG, as demand is high • Arrange timely supply of Fodder seeds Technical inputs • Organize 50 landless families into SHGs/UGs • Link SHG/UG with CPR planning and development • Negotiate/sign MOU with GP for usufruct rights
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
Total private land = 1375 acres - 1025 acres is with large and medium farmers (90 families) - 350 acres is with small and marginal farmers (110) families • Cropping intensity = 31% • Principal crops: - Paddy = 56% - Castor = 25%
• Prioritise land and water management measures on lands of small and marginal farmers • Promote fodder development on fallow lands of medium and large farmers • Promote horticulture on lands of small and marginal farmers • Need to improve cropping pattern (paddy is water intensive) • Promote drip irrigation
•
SHGs as social capital are poor: - Only 12 SHGs - 10 are in D category: very primitive stage, involved only in saving and thrift activities - All 12 have received RF • VO does not exist
PRIVATE LAND
FARMER CATEGORY
CROPPING PATTERN
•
•
Average time spent on collecting drinking water is very high: 8 hrs/day
•
8 bore wells and 4 open wells are non-operational
• SHG/VO
DRINKING WATER
Urgent need for extensive community mobilization efforts: - Organize new SHGs - Form VO - Sign MOU with VO - Network SHGs with VO - Start RF management
Need to develop drinking water facilities: convergence with Rural Water Supply Department t • Interested families should be provided with sanitary latrines • Plan WHSs to recharge defunct bore/open wells •
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GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
Migration is high (40%): - 100 families migrate under distress migration - Employed for 2 months during each cropping season - Work mostly as wage labourers • 45 families borrow money from private money lenders • 53 families dispose of cattle under distress • 23 families dispose of land under distress •
70 women-headed households: - 40 are agricultural labourers - 30 involved in poultry • Only 15 families involved in horticulture •
•
160 children in total: - 100 not immunized - 40 not enrolled in anganwadi - 30 boys and 20 girls not enrolled in school - 140 working
•
Organize migrating families into SHGs: - Link with fodder development and horticulture on CPRs - Link with families borrowing money from private money lenders
•
Diversify livelihoods to improve coping capacity
MIGRATION
31
COPING MECHANISM
LIVELIHOOD DEVELOPMENT Promote horticulture and introduce zero energy drip • Organize women-headed households into SHGs •
• CHILD DEVELOPMENT
Convergence with Dept. of Women and Child Development programmes
The number of old has increased from 6.6% in 1991 and will reach 10.5% in 2016. In thousands, this is an increase from 4400000 to 9300000 in AP2. This age group will need to be factored into action plans, as they are often the poorest. They will be particularly vulnerable in the future, because the job-seeking population is estimated to increase greatly, leading to a rise in unemployment, and a decline in family support towards the old.
2
EPW
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32
Watershed-level action plans are created using the bottom-up approach that is essential to participatory programs like APRLP. However, data from the VIS can be combined at higher levels to create graphic outputs for planning at the district level: Map 11 Meetings of WCs, Mahbubnagar
source: CRD
It is seen that • The number of watershed committee meetings are very low in APRLP mandals: priority should be given to community mobilization
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33
Map 12 Number of bore wells dug, Mahbubnagar
source: CRD
From the map we observe that • Bore wells have been dug in dark and grey villages. In these mandals, priority should be given to water management and sprinkler/drip irrigation
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
34
5.1. Encouraging Economic, Gender and Social Equity: Identifying Gaps Below we have a summary of indicators that highlight gender and social disparities. Those areas that appear in an unfavourable light must be given special attention in order to correct glaring societal imbalances.
1. Agricultural labourers are more concentrated in the southwest ___ social inequality, greater number of resource poor
2. Female agricultural labourers are more concentrated in the north west; sex ratio indicates a greater number of women ___ male-out migration (no jobs) + poverty-stricken area + womenheaded households + children probably not in school
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
3. Female literacy and literacy sex ratio for the entire western part of the district is very low ___ gender discrimination is prevalent
Focus areas: poverty alleviation + female empowerment + social equity How? STRENGTHEN SHGs
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5.2. Strengthening SHGs: Numbers, Economics and Social Composition Map 13 Spatial distribution of SHGs, Mahbubnagar
1. It is clear that many APRLP watersheds have very few SHGs
t Map 14 SHG rotation of money, Mahbubnagar
2. Rotation of money and external assistance is low
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37
Map 15 SHG corpus and savings ratio, Mahbubnagar
source: DRDA
Map 16 SC and ST population, Mahbubnagar
3. SC and ST populations are more concentrated in the eastern part of the district ___ these SHGs should have greater SC and ST representation
source: census of India 1991
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
Map 17 Social organisation of SHGs
source: DRDA
Focus areas: Increase the number and corpus of SHGs Link SHGS to external sources of funds + utilize funds in profitable enterprises Increase representation of socially backward groups: top priority should be towards SC/ST, then BC, and then OC Information for some mandals is with DPIP/ independent NGOs ___ establish convergence between APRLP, DPIP and other NGOs/GOs
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39
5.3. Improving Education In order to create a plan for the improvement of education we need first to identify the priority areas. 1. Compare working children and school enrolment deprivation to highlight worst areas
Map 18 social and gender deprivation 2000-01
source: census of India 2001
GRAPHIC METHODOLOGY USED IN THE PGMIS OF APRLP’s WATERSHED DEVELOPMENT PROGRAMME
2. Compare female literacy and literacy deprivation based on gender Map 19 Rural female literacy 2001, AP
Map 20 Gender deprivation in rural areas, 2001 AP
source: census of India, 2001
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3. Analyse drop out rates Map 21 Dropout rate classes 1-4
source: census of India, 2001
Any plan that intends to improve levels of education must identify the areas of focus Focus areas: Emphasize gender and social equity concerns when plann on the right gives the range, text left aligned, on the left gives number of Other mandals with grey outline and white inside No village lim Main title very short like a newspaper title Subtitle 1 to give more precision Subtitle 2 to give period its • cation should produce successful farmers rather than urban unemployed
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6.
MONITORING AND EVALUATION (M&E) The pyramid structure is the basis for M&E
The aim of M&E is to improve system processes, and encourage self-monitoring. It should not solely be a policing tool. Monitoring will be in two areas : a) Ongoing watersheds : this can be replicated in all districts b) New watersheds : this is restricted to APRLP districts M & E will take place at all the institutional levels of the watershed programme: 1. At the watershed level (SHGs, UGs, WSA, VO, WSC) 2. At the WDT /PIA level 3. At the PD level 5. At the CRD level
6.1. M & E at the watershed levels Monitoring and evaluation at this level will be accomplished through • Simple graphs describing the situation in the watershed • Simple graphs comparing the watershed with the best and worst watersheds, and the district average • Simple graphic tables linking different parameters, and identifying priority needs o Assessing the functioning of SHGs o Identifying the potential to develop CPRs o Identifying potential users of CPRs, and potential conflicts over access to CPRs (e.g. should common land be given to the the landless, or used for livestock?) o Considering possible actions o Working out micro-plans
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This simple graph below describes the performance of the Siddarampur watershed as compared to the district averages for watershed performance. Fifteen indicators have been divided into four categories. The first seven indicators represent the performance of the watershed in the area of transparency and participation, the next three indicators describe how well the watershed functions during the implementation stage of the watershed plan, the next two indicators are for functioning during the post-project phase, and the last three indicators describe how well the watershed functions in institutional and livelihood areas. SIDDARAMPURAM WS B.K.SAMUDRAM Mandal 1 V.Chennakesava Reddy PIA
2
3
4
Regularity of meetings Attendance in meetings Involvement of UG TRANSPARENCY
Preparation of actionplans
& PARTICIPATION
Discussion on proposals Works allocations Transparency & Accountability Monitoring of works
IMPLEMENTATION
Works Payment Records maintenance
POST PROJECT INSTITUTIONAL & LIVELIHOOD
collection of WDF Maintenance of structures women's involvement in wc Training livelihood intervention
Gap between watershed and district Better performance than district
The grey bars represent the district averages for watershed functioning. The black areas indicate where the Siddarampur is below the district average, and the white areas indicate where it is above the district average. The numbers 1, 2, 3 and 4 measure performance, and stand for very low, low, good and very good, respectively. In the category of Transparency and Participation, Siddarampur is functioning at level 4 (very good) in regularity and attendance of meetings. In involvement of UGs, preparation of action plans, and discussion on proposals it is functioning at level 3 (good). Even thought it is slightly behind the district average when it comes to involvement of UGs, this is not a matter of great concern, since it is still in the ‘good’ category. However, when it comes to work allocations and transparency and accountability, it is below the district average, and is functioning at a low level. When considering the performance of a watershed, it is not enough to simply observe whether it is above or below the district average. We also have to keep in mind whether the district average itself is satisfactory or not. For example, the district average for attendance in meetings is very good, so whether Siddarampur is slightly ahead or behind is not that important. However, when it comes to transparency and accountability, the district average is already low, and the fact that Siddarampur lags behind even this low average shows that its levels of transparency and accountability are extremely poor. In the Implementation category, the district average is poor, and Siddarampur is lagging behind for every indicator.
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In the Post Project category, Siddarampur is below the already low district average when it comes to maintenance of structures. However, in the collection of WDF it is slightly higher than the district average, and has reached level 3 (good). In the Institutional and Livelihood category, the district average for women’s involvement in the WSC is high, and Siddarampur’s performance is even better (it has reached level 4, which is ‘very good’). However, when it comes to training and livelihood intervention, the situation in Siddarampur is dismal. The district averages for these two indicators are slightly above ‘low’, but Siddarampur’s averages have dropped all the way to ‘very low’. When analysing the performance of Siddarampur through this graph, we can see that with regard to involvement and participation in the preliminary stages, the watershed functions well. However, when it comes to follow-up and maintenance, the performance falls short. They need to be trained in the practical aspects of implementing the watershed plan: work allocation, accountability, monitoring of work and payment, and maintenance of records and structures. The livelihood aspect is also of serious concern. Since one of the main aims of APRLP is to encourage sustainable rural livelihoods and improve conditions for women and the most disadvantaged sections of society, much more effort must be put into training and livelihood interventions. Focus on: • Work allocation, transparency and accountability • Project implementation (monitoring and maintenance) • Livelihood aspects (training and livelihood interventions)
6.2. M & E at the PIA level • •
•
At the PIA level we make use of Graphic tables to visualize all the watersheds, and immediately see what type of action is needed Graphs showing combined data from all the watersheds, for each theme (e.g. SHGs, WSC, drinking water, CPRs, migration, agriculture etc.) to assess the situation and monitor progress Graphic outputs or maps that link information for a particular purpose, and visualize the geographical distribution of a situation to ensure better co-ordination when planning
44
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Below is a graphic table that depicts the performance of all the watersheds under certain PIAs. The performance categories are: 1 = very poor (black) 2 = poor (dark grey) 3 = good (grey) 4 = very good (white) This type of table allows us to immediately see what type of action is most urgently needed: • Cherlopalli organizes regular meetings, but without involving UGs, discussions and planning _ they need to mobilize people • Gondireddipalli, Nagasamudram-B and Siddarampuram did not have training _ they require capacity building exercises and livelihood interventions • Gaddamnagepalli has regular meetings with good participation, discussion and planning, but absolutely no follow-up when it comes to actual implementation, monitoring and maintenance _ they need training and capacity building in these areas • In general, the poorest performance is in the implementation and post-project phases. Almost all the watersheds need to improve in this category 0
25
Regularity of meetings
50
75
Attendance in meetings TRANSPARENC Y & PARTICIPATION
0
25
69
50
INSTITUTIONAL & LIVELIHOOD
0
62
38 46
69 8 8
23
0 0
8
8
0 69
69
8
8 0
54
31
women's involvement in wc
livelihood intervention
0 54 77
Works Payment 0
Training
0
15
Records maintenance0
77 31 31 38
50
0
46
Monitoring of works0
25
0
15
77
0 0
8
85
Discussion on proposals 8
Maintenance of structures
75
8
77
Preparation of actionplans 8
collection of WDF
VERY LOW
50
0
15
0 Transparency & Accountability
POST PROJECT
75
25
0
15
Works allocations 0
IMPLEMENTA TION
0
31 85
Involvement of UG
LOW
HIGH
VERY HIGH
0
8 0
38 31
23 23
This graph combines data on the performance of all the WSCs under a particular PIA. Performance is rated as very high, high, low and very low. All percentages up to 50% are in grey. Anything over 50 % is represented in black. For example, in the ‘regularity of meetings’ category, 69% of WSCs have a ‘very high’ performance, and 31 % have a ‘high’ performance rating. Black areas in the very high and high categories are a good sign, because they indicate that over 50% of the watershed committees are performing well. However, black areas in the low and very low categories are a bad sign, as they indicate that the majority of WSCs are performing poorly. From this graph we can immediately see that the weakest points of WSCs are work allocation, transparency and accountability, monitoring, payment, and maintenance of records and structures. Immediate action in these areas should be taken. The black areas are not the only things to look out for. Grey areas in the low and very low categories are also a bad sign. For example, at first glance training and livelihood interventions might not seem to be areas that need immediate action. However, when we add
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up the grey for these areas we see that 61% of WSCs have very little training, and 54% have inadequate livelihood interventions.
6.3. M & E at the PD level PIAs are indispensable facilitators in the watershed programme. It is necessary to monitor their functioning in order to improve strategies, and ensure that action plans are effective. PDs should use QPR data to compare PIA progress with watershed guidelines, ensure that it is in accordance with the program phases, and compare watershed expenditure with prescribed yearly expenditure. The following graphs combine data on each PIA, to compare the PIAs’ progress. All values that are above average are shown in black, and values that are average or below average are shown in grey. ANANTAPUR: EXPENDITURE ON EACH ACTIVITY AS A PERCENTAGE OF THE TOTAL PLAN, PIA WISE
Assuming the above graph is for the first year (probation phase) of a watershed plan, we can make the following observations: • PIAs like Accion Fraterna and FVF are spending too much money on cemented WHSs. During the probation phase expenditure should only be on cost effective, noncemented WHSs for in situ water and soil conservation. • Expenditure should be on CPRs, horticulture and non-cemented WHSs __ PIAs should be following the probation phase guidelines. From the graph it is evident that expenditure on CPR is minimal. • Some PIAs have already used up the bulk of the budget __ the most effective PIAs are those that are obtaining maximum benefits while using the least money Expenditures should be justified by results in reality. This graph gives an idea of how PIAs are allocating money, and can be used while verifying results. Analysis of PIA functioning with respect to expenditure can lead to valuable case studies: how is it some PIAs spend less money and are successful? Highlight such case studies to find positive lessons.
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ANANTHAPUR: COST PER UNIT OF ACTIVITY, PIA WISE
The following graph shows the price/unit of activities undertaken by PIAs. • Costs/unit should be kept as low as possible. PIAs with high costs/unit are not performing satisfactorily. • PIAs in the 3rd and 4th group are the least cost effective: they have the highest costs/unit, and have spent the most of amount of money per watershed. The functioning of PIAs can also be assessed by graphs that depict the number of beneficiaries/unit. The following graphs are for non-cemented WHSs: • Ideally, CPRs should benefit the most number of people, for as low a cost as possible. Average CPR area per benificiary 30
area/ben
25 20 15 10 5 0
PIA
area/ben
In order for PIAs to be effective, watersheds under their purview should be located close to their headquarters. Similarly, to maximize administrative efficiency, MDT headquarters should be located centrally with respect to watersheds in their region.
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Map 22 MDT jurisdiction, Mahbubnagar
source: CRD
•
The above map shows that most MDT headquarters are located towards the corners of their jurisdictional areas. Since there offices are not centrally located this quite likely presents operational hurdles, making it difficult for MDT to interact regularly with watersheds that are situated at a distance from the headquarters.
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6.4. M & E at the CRD level This consists of analysing progress at the district level, and comparing the work done by the PDs. Below are some examples of how this is done. By creating graphs of the percentage of watershed money spent on different work components a profile can be got revealing differences within districts (eg, prakasam and Nalgonda spend a greater percentage of their funds on CWHS than the other 3 districts) as well as in the overall picture of expenditure on works (eg, horticulture commands only a small portion of total work funds). DISTRICT WISE % OF EXPENDITURE ON VARIOUS WATERSHED ACTIVITIES COMPONENTS (In %)
ATP
KNL
PRK
NAL MBNR
CEMENTED WHS
21,00
26,30
36,90
33,63
22,15
NON-CEMENTED WHS
20,30
14,72
26,16
24,00
11,60
SOIL CONSERVATION
21,60
32,29
12,10
19,22
38
AFFORESTATION
11,99
14,18
4,00
7,36
12,00
HORTICULTURE
3,40
2,40
3,68
5,07
8,00
21,71
10,11
17,16
10,72
8,25
OTHERS(EPA,CCT,OTWHS)
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The pie charts above and below serve only to show that bar diagrams are visually far more effective means of visually transmitting information. It is clear that bar diagrams permit an inter-district and inter-component comparison that is self-explanatory and needs no translation. On the other hand, to extract meaningful comparisons the pie graphs require a more laborious reading of their contents. The rapidity and precision with which we can draw conclusions from the bar diagrams stems from the fact that they allow an immediate visual transmission of information, based on an easily comprehensible and precise system of proportions. Pie charts do not contribute to the comprehension of the existing data table.
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% of Expenditure Component Wise
Cost/unit Component Wise
The bar diagrams above tell us, for eg, that the cost per unit of WHS is the highest in Prakasam. With respect to the percentage of watershed funds expended on WHS, Prakasam and Nalgonda display the highest figures.
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Farmers Benefited District -wise
From the right graph we see that the number of small farmers benefited per lakh of expenditure is the lowest in Anantapur. On the other hand, in the left graph we see that the percentage of small farmers benefited to total number of farmers is actually the lowest in Mahbubnagar district.
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7.
WATER MANAGEMENT
7.1. An overview Water is the most valuable Common Property Resource, and effective water management is one of the chief goals of APRLP. These few reminders from a KAWAD report summarise the impending water crunch that we will face in the not-so distant future.
Remarks from « Water Resources Audit », KAWAD REPORT 17 JUNE 2000 Water demand for domestic and livestock purpose is likely to double in the next 30 years. There has been a dramatic increase in ground water extraction for irrigation. Competition between agricultural and domestic uses will increase. It happens already in years of low rainfall. When this happens, it is generally the poor that are hit the hardest. Runoff is low, between 2 to 6 %. So there is not much scope for water harvesting. Focus should be on water management.
AP water vision, a shared water vision, released in July 2003, reveals disturbing facts. Available water resources in A.P. are about 108 billion cubic metres (bcm) Usable amount is not more than 78 bcm Present total use is about 62 bcm Water use by 2025 is expected to touch 113 bcm In other words, by the year 2025, Andhra Pradesh’s water resource demands will have met and even exceeded the available supplies.
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• •
• •
Prioritise water needs : Agriculture vs. Drinking People vs. Cattle Focus should be on water management, since there is no guarantee that the water table will rise as a result of water harvesting structures. There is a need to plan and budget water resources right from now, since the water situation in the future will only deteriorate. Water quality is also likely to deteriorate in the future, and this will be very difficult to improve. There will be an increasing scarcity of potable water. With regard to potable water, it is essential that we collect information on the levels of pesticide in drinking water. In addition to pesticide residues drinking water also needs to be tested for organic and inorganic compounds and heavy metals. Polluted drinking water is a problem that is only going to worsen in the future.
Change in net area irrigated by source: area under surface irrigation (canal/tank) has stayed almost constant, at 25 lakh ha, from 1959-1999. However, area under groundwater irrigation has increased from 3 to 18 lakhs. We need to get data on groundwater from the Groundwater Department Map 23 groundwater status Mahbubnagar
source: groundwater department, AP
Over developed critical • The groundwater status in Mahbubnagar is serious: a lot of regions are over-developed, critical, and semi-critical
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Map 24 water level increase, Mahbubnagar
source: groundwater department, AP
•
In spite of installing cemented and non-cemented water harvesting structures, in a large number of watersheds there has been little or no water level increase Map 25 wells rejuvenated, Mahbubnagar
source: groundwater department, AP
It is observed that: • In spite of water harvesting measures, most watersheds have been unable to rejuvenate the majority of their wells
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7.2. The worrying state of groundwater and tank irrigation At present both the official and the public’s attitude toward groundwater exploitation is falsely optimistic. Technically, Andhra Pradesh has only developed 42% of its total groundwater potential. It would appear, therefore that there remains a large unutilised potential for future development. With the additional number of wells that have been deemed feasible the level of groundwater development targeted is 85% of the utilisable potential. Both Telangana and Rayalseema have a limited potential for bore well irrigation. Of the 22.23 lakh bore wells in the state over 18 lakh are present in Telangana and Rayalseema. From the graph below it can be seen that the overwhelming majority of bore wells are found in Telangana.
With the sharp increase in bore wells between 1975 and 1999 the surface area irrigated per bore well actually declined from 1.22 acres to 1.19 acres in the same period pointing toward declining water yields. Despite the apparently large potential that remains to be exploited even official estimates paint for us an alarming picture. Of all the mandals in the state 336 have been identified as problematic. The water table has gone down by 20 metres in 79 mandals. In the period between November 2000 and Feb 2001 the water table fell by 2.28 metres in Telangana and by 1.52 metres in Rayalseema. The situation is particularly worrying in Telangana and Rayalseema where the absence of canal irrigation increases the burden on underground reserves of water. The number of wells is increasing at the rate of 56,000 per year and 60% of these are drilled in the Telengana region, alone. While the state as a whole has to put its effort into reviving tank irrigation, in Telangana and Rayalseema the matter is one of crucial importance. Tanks must be revived in this region and steps must be taken to replenish groundwater levels through water harvesting structures. The rate at which bore wells are being drilled shows no signs of slowing down and the drying up of open wells only confirms the depletion of groundwater. Of the 70,000 tanks in the state as large a percent as 69% are under repair. This accounts for 82% of the area under tank irrigation. It is, therefore vital that these tanks be repaired with greater efficiency and restored to their original working order.
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It would be helpful to look once again at the map below
•
Bore wells have been dug in dark and grey villages. Digging more bore wells will not solve the water shortage. WATER MANAGEMENT is the only solution.
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8.
The use of graphical techniques: Bertin and his contribution
8.1. Why represent information visually It is an indisputable fact that the graphic representation of data is the quickest and most effective way of communicating certain types of information. This is because the transmission of data is visual and immediate and does not need to be ‘read’ in order to be understood. Good graphic representation does not need to be decoded. However, to achieve this level of efficiency and precision in graphic representation there are certain techniques that must be applied. Like any other discipline graphics has its own set of rules making visual representation of information effective to a greater or lesser degree depending on how closely, but also intelligently, these rules are followed. Bertin’s contribution The founding father of graphic semiology as we know it today, Bertin helped structure and streamline the science of graphics. Bertin was the first to emphasise the simplicity and the very elemental nature of graphic communication, maintaining, nevertheless, that there are rules that should not be transgressed. These are the rules that determine whether a particular method of visualisation is superior or inferior to another. In other words, there are graphic methods that are significantly more efficient than others, facilitating an optimum visual perception of a set of data. Bertin’s method helps us to reach this optimum level of visual perception.
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8.2. Mathematics and Graphics: two types of perception3 Both graphics and mathematics start from sets previously defined in verbal or visual terms and not subject to further discussion. An equation only makes sense when the unique signification of each sign is recognized. A graphic only makes sense when the given sets proposed for study are recognized. Difference between graphics and mathematics: Graphics utilizes the three dimensions of the image. It is a spatial “sign system” independent of time. Mathematics utilizes the two dimensions of sound. It is a linear “sign system” defined by time.
Remember that written notations of music, words and mathematics are merely formulae for the memorization of fundamentally auditory systems, and that these formulae do not escape from the system’s linear and temporal character. The ear can hear an equation on the telephone; it cannot hear a map. In a moment of perception, the ear perceives one sound. In a moment of perception, the eye perceives the relationships among the sets. No other system of perception possesses this property, and it seems indeed that logic is based on the three dimension of visual perception. To utilize the immediacy of the image in order to represent a set of relationships is the main problem of graphics. We utilize graphics to save time and consequently memory; in order to SEE, that is to perceive immediately. Accordingly, a graphic which must be READ, that is perceived over time, does not solve the problem. Moreover, we observe that such a graphic is usually not even read. The reader prefers the written text, since it generally yields a much better ratio of information received to time spent.
3
Jacques Bertin, Graphics and graphic information processing, Walter de Gruyter, Berlin, New York, 1981
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8.3. A graphic! To do what? Levels of information Information is a relationship between two elements or between two sets of elements. In graphics, it is the answer to a question. What are the questions we can ask of a data table?
We must distinguish three levels of questions, that is three levels of information: The elementary level: in a given district, how much, i.e. in Prakasam how much percentage is not cultivated? The answer is provided by the number in the cell, 20%. This is elementary information, and information processing must struggle against it. In fact, our memory cannot retain the multiplicity of elementary data. We must reduce this multiplicity, discover similar elements, group them, and class them. These are the prerequisites to understanding and deciding. The overall level of the entire set is thus the true objective. What are the specifications of the districts that the entire set of characteristics constructs? There are districts with more forest, districts with more uncultivated area, districts with more cultivated area, and districts with both uncultivated and cultivated area but less forest and tree crops. This is the level necessary for decision-making. The intermediate level comprises all the subsets included between these two extremes: i.e. what are the characteristics of Mahbubnagar? What is the distribution of % Forest? The intermediate level enables us to analyze the overall relationships. Y Overall level Intermediary level Intermediary level
Elementary level
Z
X The processing of a data table reduces the x and y sets to the groupings constructed by the z numbers. Understanding is the discovery of these groups and the relationships they engender. This is the main problem in information processing
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8.4. Properties of the image Faced with this problem of data reduction, the image possesses three properties. The image has three dimensions: x, y, and z. Any point in an image can be perceived as the correspondence between a position along x, a position along y and an elevation in z. The set of points can be perceived as the set of correspondence among the dimensions x, y, and z. A meaningful form is independent of the number of its parts. The eye SEES a tree whatever the number of leaves. The image accommodates a very large number of elementary data, limited only by the “threshold of differentiation” and by material conditions. This is how the eye can immediately see the difference in distribution between two maps each comprised of twenty-five million data items. The eye can focus either on an element, or on a group or on the overall image. It can SEE a leaf, a branch or the entire tree, and it sees the relationships they engender. The eye perceives the three levels of information spontaneously. With respect to the plane, the eye possesses the property of ubiquity. But the image has only three dimensions. Can we superimpose several images? Can we superimpose several different characteristics on a map and still retain the properties of the image? No more than it is possible to superimpose several photos on the same film. The image has only three dimensions. This is its limit. Except for extremely simple forms, the superimposition of several images destroys each of them. We must use a more elementary level of reading, which excludes perception of the overall form of each characteristic and activates the memory.
8.5. Types of data Before going deeper into the techniques of graphic representation of information it would be necessary to classify the different categories of data. Any graphic construction originates with a data table. This tables comprises of rows, or objects, such as districts or watersheds, and columns or indicators, such as areas under different categories or percentage of each category to the total area. We will see later that to each type of data we can link a particular visualisation.
8.5.1. Quantitative There are two types of quantities, absolute quantities and relative quantities (or ratios). The first category refers to numbers such as population, expenditure, area, etc. The second category refers to ratios such as population density (population / area), sex ratio (female population / male population x 1000), literacy rate (literate population / population >6 years x 100), cost per unit (expenditure / area or numbers) or percentages such as % population below 7 years (population below 7 years / total population x 100), % expenditure on non cemented structures (expenditure on non cemented structures / total expenditure x 100), these are proportion of a total. Ratios and percentages imply a calculation between two absolute quantities. Do not mistake the percentage of one object to the total of the absolute quantity as a ratio. For example if the population of Mahbubnagar is 35 lakhs, and the total population of Andhra Pradesh is 757 lakks, Mahbubnagar represents 4.6 % (4.6 hundreth ) of A.P. This is not a ratio, it does not imply a calculation between two absolute quantities. It is only a way of normalising the data on a common scale of 100. To avoid any confusion one need only to return to the origin of the numbers. How are they calculated? For a ratio, the correct designation is simply the expression of the two terms
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or the ratio. For hundreth, percentages or indices we must be able to answer the question: “for one hundreth what?”. If it is hundreth of one variable it is quantitative data (ex. One district to the state total), if it is percent of a second variable, it is relative data (ex. Population below 7 years as percent to total population in one district).
8.5.2. Qualitative Qualitative data is not characterised by numbers. Types of soils, or quality of water, or the result of a typology is qualitative data. Qualitative data is theoretically not ordered, but one can often find a hierarchy. For instance soils can be ordered according to their fertility, or water can be ordered according to its sanitary quality, or the result of a typology can be ordered according to priorities of the project.
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8.6. The building blocks of Bertin’s graphic language: visual variables Like any other language there are a set number of letters in Bertin’s alphabet. These form the building blocks of all graphic constructions, helping to generate relationships of resemblance, order and proportion. Such blocks are known as visual variables and there are eight of them. The first two variables are the 2 dimensions X and Y of the plane. 1. A mark on a sheet of paper can appear in different positions depending on its X and Y. 2 . A variation in luminous mark brings in Z a third independent dimension. 3. The image is constructed on three independent dimensions X, Y and Z Apart form the change in position the above marks can also vary in Point Line Area Size Value
Texture Colour Orientation Shape
The above six variables are the visual variables of the third dimension (Z).
8.6.1. Ordered and dissociative variables Size is “quantitative” (Q). A difference in size can portray a proportion between two magnitudes: A is twice as large as B. B is the unit which serves to measure A. White cannot serve as a unit for measuring black. Consequently, value is not quantitative, value is ordered (luminous intensity from white to black, or light to dark shades of one single colour).
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Size and value are ordered (O). There is no need for a legend when putting sizes or values into order. They create an immediate order which obviously must correspond to the order of the data. To not make the visual order correspond with the order provided by the data would be a grievous irresponsibility on the part of the designer. Size and value impose their order to all other variables. Size and value are dissociative: they provide an excellent differenciation among different characteristics. They create an image. But they also construct a visual hierarchy which favors a particular characteristic. This hierarchy is an error when unjustified. The following example shows the misuse of visual variable. From the top map one cannot make out where is the expensive land. It is not a map to be SEEN. It is a map to be READ, one has to read the legend, then search for the X symbol on the map One should ask oneself this first question: what is the data? Answer is francs per square meter. Is it quantitative? Yes. Is it ordered? Yes, from cheap to expensive. What is the implantation: point, line or area? Point. Then one has to select size in the list of visual variables. The bottom map shows at a glance where is the more expensive land. Map 26: Land value in eastern France
Size: Map 13 (spatial distribution of SHGs) uses varying sizes of points to represent the number of SHGs in habitations. A larger point denotes a greater number of SHGs. Size is thus ‘quantitative’. A difference in size portrays a proportion between two magnitudes. Value: Map 4 (agricultural labourers) uses the changing values of the colour to represent the percentage of agricultural labourers (to total workforce). A deeper tone of the colour has a higher value and signifies a higher percentage of agricultural labourers. Value thus serves to portray a visual hierarchy of information. Value cannot be used to represent absolute quantities. In map 14 it is only by increasing the size of the points, and not by changing the value of their colour, that the data can be transmitted.
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Maps using size can be easily compared. It can be immediately SEEN from the two maps below that there is a geographical complementarity between the geographical distribution of scheduled castes and scheduled tribes. Map 27: Rural population in India, 1991
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One has to use VALUE to show relative quantities (Qr). Here we look at the proportion of SC and ST in the total population. One can combine orientation and shape as secondary visual variables to give more clarity to the map. Map 28: Proportion of S.C. and S.T. in India
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8.6.2. Selective and associative variables The selective or differential variables (as opposed to dissociative): texture, colour, orientation and shape give the same visibility to each characteristic; they are associative. They can thus combine with size and/or value without modifying their properties. Texture: this visual variable refers to the ‘photographic’ reduction of a given pattern. Note in the example below that the pattern does not change. Note also that the ratio of black to white remains constant even when the texture becomes ‘finer’. Texture can thus be used to establish an order in a set of data without imposing a hierarchy of value by giving more visual weight to certain characteristics over others. Texture is: Selective: as in the example below: it enables us to distinguish among areas carrying the same visual weight (areas that possess the same visibility) Transparent: it allows us to differentiate superimposed areas. Associative: it can be associated with value (see map 6 rural working children, map 37 Typology of occupations) Ordered: it has the remarkable property of creating an order among characteristics without downplaying any one of them. In the example below all the areas are equally visible yet ordered by their texture. Note that neither the pattern nor the ‘value’ (ratio of white to black) of the pattern changes.
Colour: there are ample examples of this variable. Pure colours offer maximum selectivity, that is, each of the characteristics represented stands out on its own without, however, following any hierarchy of value. The pure colours do not all have the same value. In fact the pure colours trace a V in table 1. Pure yellow is lighter than pure blue.
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Let us consider the information in (5). Transcribed by values in (6), it produces a east-west image (7). Transcribed by the order of the spectrum (8), it produces a north-south image (9). We note that it is impossible to disregard this orientation. It is as if the eye assimilates the two extremities of the spectrum as one unified perception, contrasted by the unity formed by the central colours. This is because the extremities are dark, whereas the central colours are light (8 and 3).
Perception of values dominates perception of colours; for example, a blue and a red of the same value are first seen as similar before being seen as different. If this proposition is true, the legend that orders the colours according to their value (10) will produce an image conforming to the distribution of the information. This is confirmed by image (11).
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An example of this type of selectivity can be seen in map 22 (MDT jurisdiction) Since the data represented is qualitative the cartographer has avoided establishing a visual hierarchy. By changing the value of the colour a visual hierarchy can be created for the representation of ordered data. This tonal variation in colour is known as a colour gradient. In a colour gradient it is the value of the colour that changes, bestowing greater visual weight according to the hierarchy of the component. In map 5 (female agricultural labourers) it is the value of the colour that changes to establish a visual hierarchy. Colour can be used to differenciate between the types of social groups as in map 18, grey for deprivation in all groups, red for deprivation in BC, SC and ST, blue for deprivation only in SC and ST, green for deprivation only in ST. If one combines colour and orientation, the effect is more effective than by using colour alone. Value has been combined to defferenciate intensity from high to moderate. Orientation: by changing the orientation of a pattern we gain a selectivity that is often equal to the selectivity of colour and superior to the selectivity of shape. In Map 6 (rural working children) in order to increase the selectivity between districts that are worse off (greater percentage of working children) and districts that are relatively better off (smaller percentage of working children) the orientation of the line pattern has been changed (by 90°). At a glance, it is therefore easy to differentiate between these two general categories by virtue of the different pattern orientations. It is seen that within each category a value hierarchy has been created to indicate a progressively increasing percentage of working children.
8.6.3. Purely associative variable Shape: this particular variable is very difficult to use. While it would appear that there is an infinity of different shapes at our disposal, the use of different shapes is, in fact, bound by very restrictive conditions. Shape has absolutely no selectivity. Furthermore, even elementary shapes (such as circles, triangles and rectangles) are identifiable only above a certain size. It is not easy to draw numerous differentiated shapes or to tell them apart easily.
Note the progressive difficulty in telling the shapes apart as they grow smaller The shape variable should be used with prudence and caution. There are, however, certain logical compulsions. The circle for example is very effective when portraying absolute quantities, as in map 13 (spatial distribution of SHGs). The decision to use squares to represent watersheds (map 12 number of bore wells dug) also has a convincing argument behind it. Squares were found to be extremely effective for representing the 500 ha surface area of watersheds. Difficulties are encountered, however, when several shapes are used in the same map, and it is in these situations that careful choices should be made.
Here is an example of shapes which combine orientation to create a more effective image. The use of visual variables in combination is often more effective than single variables.
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For qualitative data one can use colour, orientation an texture. It will be more effective if it is combined with value. The eye perceives patches and is very sensitive to contrasts. So it is always better to use contrast, even with qualitative data. See map 10, typology of districts. The typology of sex-ratios combine the sex-ratios of other cates, SCs and STs. The map shows the result of the classification, which is qualitative, but one can find an order in the intensity of sex-ratio, from strong (equity between women and men) to weak (less women than men). Thus one can use value in combination with orientation and shape (dots and lines). Map 29: Typology of sex-ratios in India
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Similarly in the synthesis of literacy, combining literacy rate and literacy sex-ratio, one can find an order from high to low literacy and sex-ratio. Map 30: Overall literacy in India
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Even in a more qualitative typology such as the typology of occupations one can find an order from agriculture to industry and service. Here value has been combined with texture, orientation and shape. Texture has been used to differenciate the districts with purely agriculture activities from the districts with combined activities, household industries + agriculture or services. Orientation has been used to differenciate between industries and services. Map 31: Typology of occupations in India
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8.7. Page layout The presentation of any graphic poses the problem of word-image integration. In graphics the image does not exist without the word, but the word has no meaning outside of the image. Here the drawing is not an illustration; it is the main part of the demonstration. The reading of a page goes from top to bottom and left to right. So the title which is the key to a map or a graphic must be on top. It should be short and sharp, like a heading in a newspaper. The legend must be very clear. When an indicator is plotted the calculation must appear in the legend text, so that the reader knows exactly what is projected on the map. Following is an example of page layout. 1. Over-title common to all maps 2. Main title very short like a newspaper title 3. Subtitle 1 to give more precision 4. Subtitle 2 to give period
11. Colour scheme to be worked out carefully, validated at the end of March and noted by everybody 5. Other features, forest should be of a light colour
7. Scale is left for debate. If it creates problem with Survey of India mention “notional scale” or don’t put it
8. APRLP mandals to appear with black outline and lightest possible shade of grey inside (enough for print) 9. Other mandals with grey outline and white inside 10. No village limits
16. Legend title gives units and year
13. Number of completed WS with balance left
12. Write what you see on the map, short resume of salient features
6. Source of information: specify, here it should probably be CRD, write in full for outsiders who don’t know acronyms
Legend text 14. on the right gives the range, text left aligned, 15. on the left gives number of WS in each range (and possibly calculate the %), text right aligned
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8.8. The visual matrix theory Bertin’s visual matrix is one of the most powerful tools at the disposal of graphics. This matrix provides the analyst with the means to quickly and efficiently organise his indicators in order to highlight patterns and trends that would not otherwise be evident. In the visual matrix the analyst’s mathematical logic is replaced by the graphist’s visual logic. The result is immediate, effective visual communication. The information is ‘seen’ in such a manner as to permit its rapid classification. Map 10 (typology of districts) is a cartographic representation of the typology (classification) resulting from the visual matrix.
8.8.1. Properties of the plan X Y: fixed image or convertible image Table (1) shows the presence of products A, B, C… in countries 1, 2, 3… In this form or in its graphic form (2) the analysis is almost impossible. But if one moves country 2 and product D, it becomes possible to discover groups with similar characteristics (3 ) and reduce 25 basic components into three groups. The image transformation, by swapping lines and columns, based on the universal principle of proximity and similitude, defines the orderable visual matrix, the basis for Graphics matrix theory. Permutations are symbolised in (4).
8.8.2. Why this language? Levels of perception
Data is transformed into a graphic to understand. A map, a diagram are documents which can be questionned. Table (1), which gives meat production in 5 european countries can be questioned in three ways: - in X: this meat, which country? - in Y: this country, which meat? - In Z: highest percentages, where?
The elementary level (one cell) is the only one which can be memorized by our brain, at the most a cluster of seven elementary data, far from the 25 present in this relatively simple exemple. To understand we need to integrate the complete set of data. This means that we have to reduce them to a small number of look alike groups. This is the aim of information processing, through mathematics or graphics. Which are the groups constituted in X and Y? This is the fundamental question. The answer is given in the graphic (2), also called visual reorderable matrix, which reorganises the lines and columns. The 25 basic data form two groups, A and B, with an opposed structure. This is the first information. Country C is an exception, it does not fit in either of the groups. But this is important, because while negociating, and with a similar weight in the two groups, this country makes the decision. This is the second information.
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These information are not visible in the table (1) and in any of the following graphs (3). Any graphic processing starts with a double entry data table (matrix). Three questions bring out the useless constructions: 1. which are the X Y Z components of the data table (what is the basic data?) 2. what are the groups in X and in Y that are constructed by Z (what is the overall information?) 3. what are the exceptions? These three questions measure the usefulness of any information processing. They prevent the construction of useless graphs. It is only the reorderable matrix (4) which answers all questions. It is the basic construction of graphic processing. It follows the logical chain of data – matrix – reduction – exceptions – discussion – decision – communication
It organises the process of understanding.
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8.8.3. Simplified maps
Single indicator map (1) are the basis for a collection of maps which will show the geographical distribution of each indicator (agriculture, industry, service, and the total). They are exhaustive. They can be simplified (2) and superimposed (3), using value and grain. A synthetic map (3) answers all questions but looses exhaustivity. Another alternative is the reordable matrix (4) and the resulting map (5). In both cases one faces the following dilemna: - which information processing: cartographic (2) or matrix processing (4)?
- What level of simplification: how many categories and sub-categories one should retain? - Which selective vizualisation: the choice depends upon the components distribution, which have to be studied separately before defining the apropriate graphic representation. It also poses the problem of region definition when the original data has disapeared (6).
8.8.4. Communication and the graphic choice
The word “communication” covers very different situations, which often collide. To choose one’s way one needs a detailed road map which will answer the question: “at such place, what is there?”. Inventory maps superimpose n components on a single map (1). It poses the problem of selectivity and symbolic representation. To discover correlations or regional patterns, the inventory must be exhaustive abut also
answer the question: “such component, where is it?”. The inventory reorders a collection of n single indicator maps and dicovers geographic patterns (2). It represents quantities in Z, but excludes selectivity. To discuss an action plan, the map must be simple and answer both questions. The maps superimpose few simplified components (3). To the problem of selectivity one adds that of simplification.
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ABBREVIATION APRLP BC CPR CRD GO GP M&E MDT MOU OC PBAS PD PGMIS PIA PSA QPR RF SC SHG ST UG VIS VO WDF WDT WHS WSA WSC
Andhra Pradesh Rural Livelihood Programme Backward Caste Common Property Resource Commissioner, Rural Development Government Organization Gram Panchayat Monitoring and Evaluation Multi-Disciplinary Team Memorandum of Understanding Other Tribe Participatory Baseline Assessment Survey Project Director Participatory Geo-Graphic Management System Project Implementing Agency Participatory Situation Analysis Quarterly Progress Report Revolving Fund Scheduled Caste Self Help Group Scheduled Tribe User Group Village Information System Village Organization Watershed Development Fund Watershed Development Team Water Harvesting Structure Watershed Association Watershed Committee
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9.
List of maps Map 1 Gender deprivation, Mahbubnagar ................................................................11 Map 2 Dropout rate, Mahbubnagar ..........................................................................12 Map 3 Sex ratio Mahbubnagar..................................................................................13 Map 4 Agricultural labourers, Mahbubnagar...........................................................14 Map 5 Female agricultural labourers ......................................................................15 Map 6 Rural working children, AP...........................................................................15 Map 7 Marginal farmers, AP ...................................................................................16 Map 8 Access to banks, AP .......................................................................................17 Map 9 uncultivated land AP......................................................................................18 Map 10 Typology of districts, AP ..............................................................................20 Map 11 Meetings of WCs, Mahbubnagar ..................................................................32 Map 12 Number of bore wells dug, Mahbubnagar.....................................................33 Map 13 Spatial distribution of SHGs, Mahbubnagar.................................................36 Map 14 SHG rotation of money, Mahbubnagar.........................................................36 Map 15 SHG corpus and savings ratio, Mahbubnagar..............................................37 Map 16 SC and ST population, Mahbubnagar..........................................................37 Map 17 Social organisation of SHGs .......................................................................38 Map 18 social and gender deprivation 2000-01 ........................................................39 Map 19 Rural female literacy 2001, AP ....................................................................40 Map 20 Gender deprivation in rural areas, 2001 AP.................................................40 Map 21 Dropout rate classes 1-4 ..............................................................................41 Map 22 MDT jurisdiction, Mahbubnagar.................................................................48 Map 23 groundwater status Mahbubnagar................................................................54 Map 24 water level increase, Mahbubnagar..............................................................55 Map 25 wells rejuvenated, Mahbubnagar .................................................................55 Map 26: Land value in eastern France......................................................................64 Map 27: Rural population in India, 1991 ..................................................................65 Map 28: Proportion of S.C. and S.T. in India ............................................................66 Map 29: Typology of sex-ratios in India....................................................................70 Map 30: Overall literacy in India..............................................................................71 Map 31: Typology of occupations in India ................................................................72
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