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About Nemesis


Nemesis (Núcleo de Estudos e Modelos Espaciais Sistêmicos) is an acronym for a research network on spatial economic analysis supported by PRONEX (Programa de Núcelos de Excelência) administered by CNPQ (Conselho Nacional de Pesquisa) and FAPERJ (Fundação de Amparo à Pesquisa do Rio de Janeiro.

Nemesis' objective is  to model (in conceptual, mathematical, or econometric terms) the demographic, economic, and ecological processes in Brazil highlighting their spatial interrelations in the broad sense.  

Specifically, this entails analysis of the geographic patterns of settlement within the Brazilian territory, their environmental consequences, and most importantly, the assessment of the spatial impacts of government policies.  

Despite the continental proportions of the Brazilian territory, its geographic diversity, and its socioeconomic disparities, analyses of the country's development process have ostensibly lacked a systemic spatial perspective, that is, a perspective which relates the dynamics of the whole economy to the patterns of spatial interaction displayed by separate geographic and political-administrative divisions.

The common theoretical thread tying together the research network is the analysis of the spatial processes of interaction, dispersion, diffusion or dislocation, as well as of the costs of communication, migration, transmission, or transportation inherent to these processes. These concepts are operationalized by attributes such as location, distance, accessibility and vicinity, not necessarily defined in a strict geographic sense.

Theoretical concern with the spatial dimension has long figured in the social sciences agenda. More recently, however, "economic geography" has enjoyed a truly rebirth, with roots in a diverse gamut of scientific fields. Economic analysis in particular rediscovered economies of agglomeration and scale which stand in counterpoint to transportation costs in the production of spatial disparities.

Crossing the boundaries between scientific disciplines, there has been a growing awareness of ecological problems, which derives precisely from the recognition of the spatial constraints of social and environmental processes. Concepts like pollution and environmental degradation are intrinsically linked to the spatial density of stocks or flow of natural resources.

The increased ecological awareness in its turn reflects the trend towards the geographic fragmentation of social interests and political power, which are tightly bound up with the globalization of economic, social, and cultural relations. Obvious examples in the Brazilian case are recent advances in federalism and municipalism, on the one hand, and the trends towards international liberalization and integration of trade and capital flows, on the other. Within this context, the development of indicators and analysis of the allocation and spatial distribution of economic and natural resources become a top priority of scientific and political agendas.

Finally, thanks to innovations in the fields of computers and statistics - especially the development of remote sensing and Geographic Information Systems (GIS), as well as of spatial econometrics- it is now feasible to treat spatially referenced data (quantitative observations associated with points or areas on a map) with greater rigor and efficiency.  

The empirical basis of NEMESIS research network are the systematic and consistent Brazilian state and municipal level databases, comparable over time from 1970 to 1996, comprising geo-referenced information sourced from GIS, as well as socioeconomic data in tabular form available at these levels of spatial aggregation. At present, priority tasks are incorporation of 1991 Demographic Census Data and 1996 Agricultural and Livestock-raising Census Data. Plans for the future include the building up of an information systems at more detailed spatial or administrative levels (e.g., census sectors).

The systemic and genuinely inter-disciplinary character of NEMESIS' proposal is evident in its research projects , which also make it clear that the network presents an innovative, practicable proposal regarding data sources, analytical problems and statistical methods.


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