Monitoring intra-urban inequalities with GIS-based indicators : With a case study in Rosario, Argentina

Spatial inequalities together with related problems such as spatial segregation, social exclusion and social polarization are affecting urban areas around the globe and in developing countries in particular. The increasing gap between better off and worse off neighbourhoods encourages policy makers to introduce area-based policies and to fight and compensate for disparities. Geographical Information Systems (GIS) and indicators can help to monitor inequalities, target deprived areas, set priorities, and reallocate resources. Whilst many studies have been proposed to monitor urban poverty and urban sustainability, and formulation of indicators have been generated at global level; further research is needed to monitor spatial inequality within cities and link indicators with policy making. The main objective of this research is to develop a methodology that combines the use of urban indicators and GIS as a valid diagnostic and prescriptive tool to generate policy relevant information on the complex and multidimensional aspects of spatial inequalities. This research proposes a methodology to systematically monitor the most relevant aspects of intra-urban inequalities through an indicator matrix and an approach to incorporate a geographical component into municipal budget allocation. This methodology is applied in a case study in Rosario (Argentina) and it is shown how urban indicators and GIS can describe and monitor inequality aspects such as quality of life conditions and access to physical and social infrastructure. GIS-based indicators are constructed combining different data sources such as census and administrative data. The empirical analysis is embedded in an analysis of concepts and of the perception of policy makers on urban inequalities and how they deal with data and indicators in decision-making. The different GIS-based indicators selected to analyse the spatial inequality of Rosario suggest the existence of a clear and profound socio-spatial differentiation and polarisation. Only the accessibility to social infrastructure seems to favour the worst-off areas. Some advantages of GIS to construct indicators emerge from this analysis. To operationalize indicators, it is necessary to organize data, to quantify and to communicate. In this case, it was possible to integrate different data sources such as census and administrative data, quantify needs and analyze the gaps between best and worst off areas, and to generate maps to communicate and detect problem areas. It can be recommended that to succeed in the adoption of GIS-based indicators they should be able to respond to the local needs and be policy demand driven