Changing spatial perception: dasymetric mapping to improve analysis of health outcomes in a megacity
Choropleth representation has been the most widely applied method to represent rates in disease maps due to its consistency in depicting relative data. However polygons in a choropleth map may give the erroneous notion of homogenous distribution over area in cases where the mapped quantity varies in its spatial distribution. In the case of population maps, choropleth maps suggest uniform distribution of people within large peri-urban administrative areas where population is known to be unevenly distributed within the administrative units. Dasymetric mapping can provide a more accurate and detailed distribution of population data by using ancillary information to spatially disaggregate population within administrative units. We have developed a procedure to use more detailed fiscal cadastre blocks to disaggregate census data within less detailed enumeration and sample areas. Here we explain the procedure and provide simple examples of this dasymetric representation as applied to population density, socioeconomic and health indicators. This approach may help to identify fine-scale risk patterns of infectious and chronic diseases and associated socioeconomic or environmental risk factors. It is hoped that better visualization through this approach will help specialists in planning to reduce social injustice in complex urban environments.