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Data Integration Issues in Research Supporting Sustainable Natural Resource Management
Author(s) -
HERR ALEXANDER
Publication year - 2007
Publication title -
geographical research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.695
H-Index - 47
eISSN - 1745-5871
pISSN - 1745-5863
DOI - 10.1111/j.1745-5871.2007.00476.x
Subject(s) - natural resource management , geography , natural resource , context (archaeology) , environmental resource management , data collection , resource (disambiguation) , census , resource management (computing) , population , scale (ratio) , data science , computer science , cartography , ecology , statistics , economics , computer network , demography , mathematics , archaeology , sociology , biology
Current decision‐making in natural resource use and management aims at delivering ecologically‐sustainable development to achieve conservation and economic benefits. The process of guiding natural resource use requires the integration of social, economic and biophysical information on which to base management decisions. This paper discusses the integration of socio‐economic information for natural resource management (NRM) planning and decision‐making in the Australian context. A comprehensive resource of socio‐economic data is the Census, which is undertaken every five years by the Australian Bureau of Statistics (ABS) for the whole of Australia. Unfortunately there are qualitative and quantitative issues stemming from the use of ABS census data maps for NRM decision‐making, as they are at a different scale to and the boundaries do not coincide with biophysical information. These issues include the variable shape of collection districts, the use of enumerated data for population‐based statistics, the large size of collection districts in low populated areas, and the averaging of socio‐economic information over the collection districts. Examples highlight these issues and show a way forwards in improving data integration, which includes simple spatial overlay methods and regression modelling.