Multi-criteria approach to geographically visualize the

International Journal of Sustainable Development & World Ecology

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Multi-criteria approach to geographically visualize the quality of life in India Mridu Prakash, Roopam Shukla, Anusheema Chakraborty & P. K. Joshi To cite this article: Mridu Prakash, Roopam Shukla, Anusheema Chakraborty & P. K. Joshi (2016): Multi-criteria approach to geographically visualize the quality of life in India, International Journal of Sustainable Development & World Ecology To link to this article: http://dx.doi.org/10.1080/13504509.2016.1141119

Published online: 19 Feb 2016.

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Date: 19 February 2016, At: 05:23

INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT & WORLD ECOLOGY, 2016 http://dx.doi.org/10.1080/13504509.2016.1141119

Multi-criteria approach to geographically visualize the quality of life in India Mridu Prakasha, Roopam Shuklaa, Anusheema Chakrabortya and P. K. Joshia,b Department of Natural Resources, TERI University, New Delhi, India; bSchool of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India

Downloaded by [Anusheema Chakraborty] at 05:23 19 February 2016

a

ABSTRACT

ARTICLE HISTORY

Quality of Life (QoL) is the description and evaluation of coincidences among social, economic and ecological conditions in a particular community, locality, region or a country. By categorising regions according to their QoL, one can examine and assess not only the possible impacts of development programmes, but also the pressure from environmental degradation processes. This study maps QoL at the sub-national level (districts) in India, conceptualized under three pillars of sustainable development. The assessment uses 10 subindices constructed using 54 indicators (49 from Census of India database and 5 remote sensing inputs). Recognising that not every indicator is of equal importance, analytical hierarchy process (AHP) was used to assign weights to the indicators and sub-indices. Furthermore, geostatistical Moran’s I clustering was done to assign priority to QoL classes. Distribution of high QoL shows correspondence with the network of national highways throughout the country. Significant dependence of QoL was observed with urban population (r2 ~ 0.75–0.95), rural population (r2 ~ 0.75–0.98) and Human Development Index (HDI) (r2 > 0.7) for different states. The geostatistical analysis identifies clusters of districts which can significantly improve the living conditions with priority actions, and where interventions and long-term planning would be required. The results of this study can serve as the basis for targeting prioritization efforts, and policy interventions at district level for improving QoL and, perhaps, achieving Sustainable Development Goals (SDGs) as well.

Received 7 October 2015 Accepted 2 January 2016

1. Introduction Quality of life (QoL) is a multi-dimensional concept incorporating facets of social and economic wellbeing, such as education, health, safety and security, access to basic amenities, and other aspects of life at a local level (Skevington et al. 2004; Costanza et al. 2007; Land et al. 2012). One of its connotations is the extent to which nations provide conditions deemed good for people, such as social security, economic and environmental prosperity and political stability. The others refer to the capacity of citizens to thrive in these conditions. Thus, QoL can be conditions which are ‘assumed’ or ‘apparent’ (Veenhoven 1996) for the well-being of the human society. QoL can be measured for individuals, group of people or community or a country using either a subjective or an objective approach (Haslauer et al. 2014). Objective approaches utilize data from secondary sources to measure QoL, while the subjective approach assesses QoL according to the perception of individuals (Marans 2012). QoL and well-being are linked closely to the notion of social, physical and natural capitals which broadly concern human networks, shared values and understanding that exist within and between groups, which are further supported by

CONTACT P. K. Joshi © 2016 Taylor & Francis

[email protected]

KEYWORDS

Quality of life; census; remote sensing; GIS; data integration; AHP

economic and environmental processes. QoL research needs to investigate, identify, document and report what defines well-being of communities (Cobb 2000). During the past three decades, the research on QoL has undergone various cycles of either growth or decline, in both attention and the subsequent research activity levels (Diener 1995; Estes 1997; Cobb et al. 1998; Osberg & Sharpe 1998; Miringoff & Miringoff 1999; UNDP 2001). This is partially due to dissatisfaction with the selection of indicators and monitoring approaches that ensued in QoL research. Enhanced interest in assessing QoL in the recent times can be attributed to the growing imperative to monitor the development processes, such as reporting and verifying the processes of globalization, sustainability benchmarking of living standards, and trends towards evidence-based policy-making practices. E-governance initiatives have provided new techniques and opened new perspectives for the presentation and dissemination of information on living conditions and QoL. In addition, the availability of appropriate database has greatly improved now than in the early stages QoL research (Noll 2015). In many countries, QoL-specific surveys are conducted regularly (Vogel 1997). Therefore, the QoL research is now increasingly being applied at community and city


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levels (Yuan et al. 1999; Wong 2001; Kemp et al. 2003; Nichol & Wong 2005; Li & Weng 2007). The study on the QoL in the developing and developed countries (both assessment and measurement) has gained interest from a variety of disciplines such as planning, geography, sociology, economics, psychology, political science, behavioural medicine, marketing and management (Sirgy & Samli 1995; Andrew 1999; Foo 2001; Rapley 2005). It is becoming an important tool for policy evaluation, rating of places, urban planning and management (Dashora 2009). Several frameworks have been used to operationalise the concept of QoL, among which the triple bottom line framework Sustainable Development (SD) is being used indispensably (Feneri et al. 2013). The forward-looking concept of SD addressed the QoL of present and future generations through traditional concerns with a balanced and harmonious environmental, social and economic dimension (Noll 2000a, 2000b, 2015; Land et al. 2012). With this emphasis, SD became a widely accepted term to describe the goal of achieving a high, equitable and sustainable QoL (Eckersley 1998). Despite QoL being one of the basic principles of SD (Lambiri et al. 2007), review of worldwide efforts to develop and apply methodological frameworks for SD assessment (UNCSD 1996; Devuyst et al. 2001, Kobus 2005) reveals a dearth of research on links to QoL. As far as methodological issues are concerned, the application of sophisticated methods of data analysis and presentation certainly is among the most obvious developments in QoL research. QoL being a complex multi-dimensional concept, assessments should be done using multiple criteria decision making (MCDM) for selection of indicators and assigning weights. Among the available methods, the analytic hierarchy process (AHP) is one of the most extensively used tools (Ying et al. 2007; Aghataher et al. 2008; Pourghasemi et al. 2012; Cozannet et al. 2013). AHP uses the pair-wise comparison matrix for calculating the weights of the criterion. This brings intuitive appeal to decide upon solutions for integrated processes, with provision to check inconsistencies of each criterion considered to study contribution of each indicator (Ramanathan 2001). The uniqueness of applying AHP helps in integrating situations of uncertainty without losing subjectivity and objectivity of any evaluation measure (Chakraborty & Joshi 2014; Shukla et al. 2015). The infrastructure development processes in India have greatly influenced the well-being of population and their overall QoL. With a wide variety of social, economic and environmental features, the vast diversity in India leads to different development scenarios. Hence, the characterization of spatial disparity in QoL of communities and sectors is essential for targeting and planning any policy intervention.

With this background, the aim of the paper was to examine and evaluate QoL at the sub-national level (districts) in India. QoL was conceptualized under SD framework to showcase how districts of India differ in their ability to support sustainable well-being. Using AHP as a multi-criteria decision-mapping method, QoL was assessed in terms of social, ecological and economic well-being. We also identified hotspots as cluster of districts with differential degree of interventions required using spatial analysis.

2. Study area India, officially known as the Republic of India, a country in South Asia, is situated between 6°44′ and 37°30′ North latitude and 68°7′ and 97°25′ East longitude, covering an area of 3.3 million sq. km. India is a federation composed of 29 states and 7 union territories. Each state or union territory is further divided into administrative districts totalling 640 districts. It is the seventh largest country by area and the second most populous country with over 1.2 billion people. Its population grew by 17.64% during 2001–2011. As much as 17% of the world’s population lives in India, with a population density of 324 persons per square kilometre and literacy rate of 70.04%. With 53 million-plus cities in India, around 31% of the population lives in urban areas. Agriculture is the dominant occupation of the people of India, establishing the most important economic sectors for the country. India, a developing nation, has been undergoing phenomenal changes in past two decades. Despite a rapid growth in economy, India faces sweeping social and environmental challenges. Great spatial disparity exists within India as not all districts perform equally well in generating and supporting sustainable well-being. The location of study area, districts and union territories is given in Figure 1.

3. Data and methodology A hierarchical design has been employed in this study; 54 indicators were grouped into 10 sub-indices. The sub-indices were further regrouped under the three dimensions of SD (representing social, ecological and economic indices) analogous to the dimension of QoL. The workflow starts with defining QoL and develops a conceptual framework, followed by identification of relevant indicators. All the indicators were normalized for comparative account, filling the envisaged gaps during analysis. Pair-wise comparison matrix was used for calculating the weights for each indicator using AHP. Further, the indicators were aggregated into sub-indices. Finally, mapping of QoL was carried out. The generated database was further


INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT & WORLD ECOLOGY

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Figure 1. Location of study area. Source: adapted from Wikipedia

processed using spatial statistical tools to identify hotspots for priority decision making and policy formulation.

3.1. Data As per the 2011 census data, 640 districts were considered in the study. For QoL evaluation, selection of indicators needs to be explicit. Further, assigning weights to each indicator is necessary, though difficult, considering the complex nature of QoL. Depending on the major determinants of QoL and availability of data, suitable indicators were selected. Three broad components, namely, ecological, social and economic, were considered for conceptualization of QoL. The sub-indices considered for each broad component were as follows: (1) social (community assets, housing type, and occupancy of settlements); (2) ecological (drinking water, and environment); and (3) economic (communication, household fuel, household lighting, household asset and household sanitation). In such a research, categorization of sub-indices/ indices requires an overall index from a suite of indicators to comprehend the underlying major need for a good QoL. The details of the sub-indices, and indicators considered for mapping QoL are given in Table 1. The two primary sources of the data (Table 1) are satellite remote sensing (five variables) and Census of India (49 variables). Terra MODIS (Moderate-Resolution Imaging Spectroradiometer)-derived products and DMSP-NTLS-OLS (Defence Meteorological Satellites Program – Operational Linescan System – Night Time Lights) data for 2011 were used in the present study. DMSP-NTLS-OLS products are 30 arc seconds grids,

spanning −180 to 180° longitude and −65 to 75° latitude (http://www.ngdc.noaa.gov/dmsp/).The NDVI and LST data were acquired through the United States Geological Survey (USGS) Earth Resource Observation Systems Data Center (https://eros.usgs.gov/), which had corrected the radiometric and geometrical distortions of the images. MODIS LST (product code: MOD11A1) Level 3 global 1 km grid product and surface reflectance product (product code: MOD09GQ) of bands 1 and 2 at 250 m resolution was used. The 2011 census data from the Census of India (www.censusindia.gov.in) used in this study include extracts from the tabular data on variety of statistical information on different characteristics of the people of India. The Indian Census is the most credible and only source of complete primary data on demography, economic activity, literacy and education, housing and household amenities at district level. The distribution of forest cover was taken from the State of Forest Report of Forest Survey of India (www.fsi.nic. in). The spatial data on the distribution, position and boundary of districts were collected from DivaGIS. This was used to integrate the non-spatial data to GIS environment.

3.2. Methodology We have aggregated the sub-indices under the social, ecological and economic components to compute a cumulative QoL index for the districts of India. The indicators selected were measured on different scales and few are dimensionless values, thus normalization of data was done using the standard formula:

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Table 1. List of indicators and sub-indices to compute QoL. S. No.

1. 2. 3. 4. 5. 6. 7. 8. 9.


10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39.

Indicators Description Social Community assets Banks Number of banks in each district Factories Number of factories/workshop/work-sheds in each district out of all counted census houses Hospitals Number of hospitals/dispensary in each district out of all counted census houses Housing Number of houses in each district out of all counted census houses Road Length of road network in each district Schools Number of schools/colleges in each district out of all counted census houses Shops Number of shops in each district out of all counted census houses House type HR-Bricks Number of census houses with roof made of bricks HR-Concrete Number of census houses with roof made of concrete structures HR-Grass Number of census houses with roof made from grass, bamboo, thatch, wood, mud, etc. HR-Metal Number of census houses with roof made up of G. I., metal and asbestos HR-Plastic Number of census houses with plastic/polythene roof HR-Slates Number of houses with roof material prepared from slates HR-Stone Number of census houses with roof made of stones/slate HR-Tiles Number of houses with roof prepared from tiles Occupancy HResNumber of occupied houses available out of all Occupied the counted census houses HRes-Other Number of houses that are available out of all the counted census houses and using for residence as well as other purposes like shops HRes-Res Number of residential houses available out of all the counted census houses HRes-Vacant Number of vacant houses available out of all the counted census houses Ecological Drinking water WtHousehold using water from hand-pump HandPump Wt-River Household using water from river/canal Wt-Spring Household using water from spring Wt-Tank Household using water from tank/pond/lake Wt-Tap Household using water from tap water WtHousehold using water from tube well TubeWell Wt-Well Household using water from well Environment ForestCoverb Percentage of forest cover in each district Aggregate LST in each district (in the month of LSTa May) Aggregate NDVI in each district (in the month of NDVIa August) NSA Net Sown Area in each district a Percentage of urban area in each district Urban Economic Communication HC-Radio Households with Radio/Transistor HCHousehold with telephones Telephone HC-TV Household with TV Household fuel HF-Biogas Household using Biogas as main source of cooking fuel HF-Coal Household using coal/lignite or charcoal as main source of cooking fuel HF-Crop Household using crop residues as main source of cooking fuel HF-Dung Household using cow dung cakes as main source of cooking fuel HFHousehold using electricity as main source of Electricity cooking fuel

(Continued )

Table 1. (Continued). S. No. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54.

Indicators

Description

HF-Firewood Household using firewood as main source of cooking fuel HF-Kerosene Household using kerosene oil as main source of cooking fuel HF-LPG Household using LPG/PNG as main source of cooking fuel Household lighting HI-Electricity Household using electrification as main source of illumination HI-Kerosene Household using kerosene oil as main source of illumination HI-Nil Household with no source of illumination HI-Oil Household using other types of oils as main source of illumination HI-Solar Household using solar energy as main source of illumination Household assets HT-Bicycle Household using bicycle as means of transportation HT-Car Household using car/jeep/van as means of transportation HTHousehold using scooter and motorcycle as MotorCycle means of transportation Household sanitation HS-Drink Household with access to multiple drinking water sources among river/canal/spring/tank/pond/lake, Tap water/hand-pump/tube well/well HS-Bath Household with access to bathing facility HS-Lat Household with access to sanitation HS-Drain Household which are connected to drainage facility

a

Using satellite remote sensing inputs; bData from Forest Survey of India; others from Census of India.

I¼

Ia Imin Imax Imin

(1)

where I is the normalized value of an variable; Ia is the actual value of the same indicator, with Imin and Imax representing the minimum and maximum values, respectively, of the indicator. After normalization, each indicator was assigned weights using the AHP. AHP develops priorities that best represents the respective element. It represents a problem in a hierarchical structure and judges alternatives by providing a procedure to calibrate a numeric scale for measuring the qualitative performances (Saaty 1980). The steps involved are as follows: (i) defining the unstructured problem and developing the AHP hierarchy, (ii) pair-wise comparing of the criteria using Saaty’s scale (1–9), (iii) estimating the relative weights (0–1), (iv) checking the consistency ratio (