Pattern of house vacancy in US: a case study in Indianapolis from nighttime light

Authors: Haipeng Zhao*, Indiana State University - EES, Qihao Weng, Indiana State University - EES
Topics: Remote Sensing, Urban and Regional Planning
Keywords: house vacancy, nighttime lihgts, time series, change detection
Session Type: Paper
Day: 4/6/2019
Start / End Time: 5:00 PM / 6:40 PM
Room: Buchanan, Marriott, Mezzanine Level
Presentation File: No File Uploaded

Vacant houses spread across many American cities create large blighted gap in landscape. These abandoned properties diminish value of neighbor block while impose an extra financial burden on local government. Field survey can provide detailed status for each unit but cost amount of labor and economic resources and take a long time. In this paper, a land-use based method is proposed. It is designed to estimate house vacancy rate (HVR) using light imaging data at city level. NLCD (National Land Cover Database) products are combined to fuse land-use components with light intensity based on geospatial tag. Residential areas are identified using IS (impervious surface) threshold. For each residential cluster with different developed density, pixel-level HVR is calculated based on ratio of light intensity. We applied this method to city of Indianapolis. NPP-VIIRS nighttime lights composite data of March 2013 are selected to have an experiment. The result shows a close relationship with survey data from U.S. Census. The error is approximately 0.9%.

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