Authors: Guillermo Douglass-Jaimes*, Pomona College, Robert Castro*, California State University, Monterrey Bay, Claudete Cardoso, Universidade Federal Fluminense, Ana Paula Barbosa, Universidade Federal Fluminense, Carlos Alberto Dias Pinto, Ministério da Saúde de São Gonçalo
Topics: Geographic Information Science and Systems, Urban Geography, Latin America
Keywords: Positional accuracy, slum designations, smartphone mapping
Session Type: Paper
Start / End Time: 1:20 PM / 3:00 PM
Room: Napoleon B1, Sheraton 3rd Floor
Presentation File: No File Uploaded
Slums are thought to embody the parts of cities that house poor and marginalized residents in low-income and substandard housing; conditions which are long-known to promote poor health. While Brazil, like many governments around the world, has officially classified these areas at the local and national levels, these official designations often ignore areas that face similar material and social challenges such as a lack of basic services. While improvements to the accuracy of these designations continue to be made, knowing where patients are located, and their perceptions of living in a community with slum-like conditions may shed light on the spatial influences of health that are missed when relying on official designations of slum status. Further, the proliferation of mobile applications and devices to collect geospatial data on the incidence and spread of disease can help fill that gap. This paper presents the initial findings of a pilot mapping effort conducted in São Gonçalo, Brazil, located in Rio de Janeiro State. Teamed with health professionals from the municipal health department of São Gonçalo, we present the findings of our pilot mapping project testing the accuracy, precision, and adequacy of low-cost mobile mapping applications to collect survey and spatial data relevant identifying health post locations in and around Jardim Catarina—an area perceived by most residents and health workers as a slum, yet unrecognized by the federal government as such. These tools are low-cost and easily accessible, and could thus be used for ongoing disease tracking.