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

Mingzhu Du,Shengyuan Zou,Chen Shi,Le Wang

doi:10.3390/rs10121920

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.

Highlights

The phenomenon of urban shrinkage in the Great Lakes watershed region from the 1970s to 2000s in the U.S was associated with depopulation deindustrialization [1,2,3,4]
We only focus on the vacant residential buildings, called vacant houses
Model 1 has an adjusted R2 of 0.51 and an RMSE of 0.055, and Model 2, which has the factors extracted from the Jilin1-03 satellite night-time data, achieves an adjusted R2 of 0.656 and an RMSE of 0.046

Summary

Introduction

The phenomenon of urban shrinkage in the Great Lakes watershed region from the 1970s to 2000s in the U.S was associated with depopulation deindustrialization [1,2,3,4]. The traditional industrial cities in this region suffered a rapid decline in industry and population, leaving behind thousands of abandoned residential houses [5,6,7,8]. The causes of vacant houses are complicated and contain factors from policy, economy, and society [8]. As a result [10], such a large amount of abandonment properties brought serious social security and economic problems [9]. These vacant houses act as a neighborhood safety threat by creating conditions for crime. Vacant houses served as a symbol of city shrinkage and an indicator of community security and mental health. Relevant studies are beneficial to understanding socio-economic impact drivers and managing community revitalization

Methods

Results

Conclusion