Geographic Information Issues Associated with Socio- Economic Modelling from Night-time Light Remote Sensing Data

The ability to model socio-economic parameters from remote sensing data sources is an important element in addressing the data gap that currently exists for linking human and physical systems in Earth system models. The provision of information about such parameters in a gridded format would be highly advantageous for incorporation with other environmental datasets within a Geographical Information System (GIS) environment. This paper outlines some of the geographical information issues encountered in a recent study to analyse and map the relationship between night-time radiance from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and Gross Domestic Product (GDP) for countries in the European Union and the United States. Relationships between total radiance and GDP were constructed at a number of sub-national spatial scales. These relationships were then used to create maps of economic activity for these regions at an enhanced spatial resolution of 5km. The use of calibration statistics from administrative areas of different sizes gave rise to issues such as the Modifiable Area Unit Problem (MAUP) and the Ecological Fallacy. Correlation coefficients of these relationships were found to be highly variable as a result of the MAUP. Analysis of different economic sectors revealed that the overall relationship is a combination of different relationships for the different economic sectors present in any given administrative area. This paper not only intends to highlight how one may use remote sensing data to successfully create a map of economic activity compatible for use within a GIS, but also to demonstrate the complementarity between remote sensing and GIS at every level of analysis. * Corresponding author.