Spatial Identification of Housing Vacancy in China

The housing vacancy rate(HVR)is an important index in assessing the healthiness of residential real estate market.In China,it is hardly to take advantage of the basic data of real estate information due to the opaque of those data.In this paper,the HVR is estimated to two scales.At the grid level,urban area ratio was calculated by nighttime images after eliminating outliers of nighttime images and night light intensity of non-residential pixels in mixed pixels by a proposed modified optimal threshold method,and built-up areas in each pixel were extracted from the land-cover data.Then,the HVR is calculated by comparing the light intensity of specific grid with the light intensity of full occupancy rate regions.At the administrative scale,the GCI(‘ghost city’index)is constructed by calculating the ratio of the total light radiation intensity of a city to the total construction land area of the city.The overall spatial differentiation pattern of the vacant houses in the national prefecture level administrative regions is analyzed.The following conclusions were drawn:vacant housing is rare in certain eastern coastal cities and regions in China with relatively fast economic development.Cities based on exhausted resources,some mountainous cities,and cities with relatively backward economic development more typically showed high levels of housing vacancy.The GCI of prefecture-level administrative units gradually declined from north to south,whereas the east-west distribution showed a parabolic shape.As city level decreased,the GCI registered a gradual upward trend.China’s urban housing vacancy can be divided into five categories:industry or resources driven,government planned,epitaxy expansionary,environmental constraint and speculative activate by combining the spatial distribution of housing vacancy with the factors of natural environment,social economic development level,and population density into consideration.

Natural and Socioeconomic Factors and Their Interactive Effects on House Collapse Caused by Typhoon Mangkhut

Typhoons are an environmental threat that mainly affects coastal regions worldwide.The interactive effects of natural and socioeconomic factors on the losses caused by typhoon disasters need further examination.In this study,GeoDetector was used to quantify the determinant powers of natural and socioeconomic factors and their interactive effects on the rate of house collapse in Guangdong and Guangxi Provinces of southeast China caused by Typhoon Mangkhut in 2018.We further identify the dominant factors that influenced the disaster losses.The local indicators of spatial association method was then introduced to explain the spatial heterogeneity of the disaster losses under the influence of the dominant factor.The results indicate that both natural and socioeconomic factors significantly affected the house collapse rate.The maximum precipitation was the dominant factor,with a q value of 0.21,followed by slope and elevation,with q values of 0.17 and 0.13,respectively.Population density and per capita gross domestic product had q values of 0.15 and0.13,respectively.Among all of the interactive effects of the influencing factors,the interactive effect of elevation and the ratio of brick-wood houses had the greatest influence(q=0.63)on the house collapse rate.These results can contribute to the formulation of more specific safety and property protection policies.