Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data

This paper develops a methodology for integration of remote sensing and census data within a GIS framework to assess the quality of life in Indianapolis, Indiana, United States. Environmental variables, i.e. greenness, impervious surface and temperature, were derived from a Landsat ETM+ image. Socio‐economic variables, including population density, income, poverty, employment rate, education level and house characteristics from US census 2000, were integrated with the environmental variables at the block group level to derive indicators of quality of life. Pearson's correlation was computed to analyse the relationships among the variables. Further, factor analysis was conducted to extract unique information from the combined dataset. Three factors were identified and interpreted as material welfare, environmental conditions and crowdedness respectively. Each factor was viewed as a unique aspect of the quality of life. A synthetic index of the urban quality of life was created and mapped based on weighted factor scores of the three factors. Finally, regression models were built to estimate the quality of life in the city of Indianapolis based on selected environmental and socioeconomic variables.