Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data

Abstract

The vacant house is an essential phenomenon of urban decay and population loss. Exploration of the correlations between housing vacancy and some socio-environmental factors is conducive to understanding the mechanism of urban shrinking and revitalization. In recent years, rapidly developing night-time remote sensing, which has the ability to detect artificial lights, has been widely applied in applications associated with human activities. Current night-time remote sensing studies on housing vacancy rates are limited by the coarse spatial resolution of data. The launch of the Jilin1-03 satellite, which carried a high spatial resolution (HSR) night-time imaging camera, provides a new supportive data source. In this paper, we examined this new high spatial resolution night-time light dataset in housing vacancy rate estimation. Specifically, a stepwise multivariable linear regression model was engaged to estimate the housing vacancy rate at a very fine scale, the census tract level. Three types of variables derived from geospatial data and night-time image represent the physical environment, landuse (LU) structure, and human activities, respectively. The linear regression models were constructed and analyzed. The analysis results show that (1) the HVRs estimating model using the Jilin1-03 satellite and other ancillary geospatial data fits well with the Census statistical data (adjusted R2 = 0.656, predicted R2 = 0.603, RMSE = 0.046) and thus is a valid estimation model; (2) the Jilin1-03 satellite night-time data contributed a 28% (from 0.510 to 0.656) fitting accuracy increase and a 68% (from 0.359 to 0.603) predicting accuracy increase in the estimate model of the housing vacancy rate. Reflecting socio-economic conditions, the luminous intensity of commercial areas derived from the Jilin1-03 satellite is the most influential variable to housing vacancy. Land use structure indirectly and partially demonstrated that the social environment factors in the community have strong correlations with residential vacancy. Moreover, the physical environment factor, which depicts vegetation conditions in the residential areas, is also a significant indicator of housing vacancy. In conclusion, the emergence of HSR night light data opens a new door to future microscopic scale study within cities.

Remote Sensing of Night Lights—Beyond DMSP

“…Vacant urban lands, where houses are abandoned, are a type of urban decay that signifies an economic decline. While time series of night-time lights have been successfully used in the past to monitor the decrease in economic activity at the regional and national levels (e.g., [6]), this paper [27] is the first to show the value of high spatial resolution night-time imagery for examining urban decline patterns at the neighborhood scale.…”

“…Most papers in both this special issue as well as in the scientific literature of remote sensing of night lights use coarse spatial resolutions sensors (such as DMSP/OLS and VIIRS/DNB). The only paper within this special issue taking advantage of the new generation of higher spatial resolution satellites is that of Du et al [27]. They used the commercial Jilin-1 satellite to examine its potential to model house vacancy rate within the city of Buffalo (New York, USA).…”

A Novel Sample Selection Method for Impervious Surface Area Mapping Using JL1-3B Nighttime Light and Sentinel-2 Imagery

“…Moreover, radiometric calibration was applied on JL1-3B according to the document provided by CGSTCL. The conversion parameters (Tab.III) and a conversion equation between the DN value to the radiance of the three bands is [35]:…”

Learning Geographical Distribution of Vacant Houses Using Closed Municipal Data: A Case Study of Wakayama City, Japan

“…In response to the increasing demand for vacant housing detection, researchers try to detect vacancies using various data such as satellite images, GPS tracking data and public survey results. Du et al (2018) examined census-level vacancy rate using night time satellite images. Bourne (2019) took advantage of public data by examining the value of low-use domestic properties.…”