A fishnet-constrained land use mix index derived from remotely sensed data

Abstract

Research works related to public health, transportation and urban planning have called for indices of land use mix (LUM) to support their spatial models. We propose a fishnet-based LUM calculation algorithm that works with the National Land Cover Database (NLCD) land cover data, a high-level product of Landsat satellite images. Comparing to the traditional LUM calculation, the fishnet structure can work at various spatial scales if aggregating to the administrative boundaries. Test results from regression models showed that our method was able to solve the scale problem identified as modifiable area unit problem that caused an unexpected positive correlation of obesity rate with LUM at the county scale. This is due to the fact that the existing methods do not limit the distance of LUM. The fishnet method provides a feasible way to calculate LUM indices across multiple scales. The NLCD data are the state-of-the art land use and land cover data for the contiguous United States. Our research provides a working example of the application of NLCD data or similar remote sensing products in public health-related research.

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