Does lack of access to space say anything economic?
Leaps of advancement can occur when one attempts to understand understand human interaction with the space/time. Econometrics, an amalgam of study areas like economics, mathematics, computer science and statistics for quantitative economic analysis is gaining a greater significance in the information age owing to the bulk of digital information becoming increasingly readily available. I have been working in this emerging field adding my part of geospatially enhancing the process of econometrics. Needless to mention, something beautiful came out of this endeavor to perform geospatial econometrics.
Economic characteristics are actually observable phenomena. These phenomena are observable as geospatial characteristics: in infrastructural assets and cultural practices of peoples in a given place; in other words, structure and distribution patterns of natural and cultural features with respect to their function. A variety of geospatial data, that is, digital representations of (natural: elevation, water bodies etc) and cultural (roads, buildings, bridges etc) characteristics of an area are used to measure/compute indices of socio-economic phenomena geospatially. Urban poverty is such an socio-economic phenomenon we attempted to measure/compute.
Access to free space is a luxury. Space creates expansiveness and compromising with limited space is an indication of lack of or inhibition to growth. As philosophical as this sounds, geospatial methods and analyses helped translate this simple principle into estimating the actual economic growth or lack of it in the actual. A version of this principle has been explored as: “does lack of space or having to fight for space and resources/services (which are an indirect function of space) indicate poverty”. We thus estimated urban poverty as a degree of ‘lack of access to space’ people face in order to thrive. Multiple observations of available space were used in this which included total open space, road space, and habitable space.
Geospatial estimation of urban poverty struck the cord with the traditional way of estimating poverty by way of counting (statistically estimating) actual poor people. The two ways of estimating poverty showed very high correlations corroborating the geospatial estimates. Geospatial estimation of poverty not only gave the counts but showed where the poor people are – answering a much bigger question that can facilitate better policy-making, implementation, and tracking of developmental initiatives. Our work is being published on The Mint and can be found on www.livemint.com showcasing one state every two weeks.