Abstract:With the rapid development of land marketization in China, the spatial patterns of residential land prices in different regions have become increasingly complicated. The very high and continuously rising residential land prices in many cities are causing significant challenges to economic development and social stability. Yet, there has only been a limited amount of attempts made to model and analyze the regional dynamic changes of residential land price systematically, especially in term of the spatially varying effects of key demographic and economic factors. In this study we provided a perspective analysis of the changes of residential land prices in 2008, 2011 and 2014 based on the land price monitoring records of 105 cities and then conducted a geographically weighted regression (GWR) analysis on the relationships between residential land price and three major impact factors (i.e., immigrant population, gross domestic product (GDP) and investment in residential buildings). Results show that the areas in which GDP had relatively strong positive impacts on residential land price expanded with time. The negative effects of immigrant population on residential land price were mainly concentrated in the cities around the Bohai Rim and the area with negative effects gradually shrank in the three studied years. Conversely, the areas with negative correlation between investment in residential buildings and residential land price gradually expanded in size over time. A geographical detector was used to examine the relative importance of factors to residential land price. It was found that the GDP had more significant influence on residential land price than other factors and the influence of the three factors to overall variation in residential land price increased over the three studied years. These results underscore the importance of taking spatially varying effects of major driving factors into account in policy-making on regional land market.

One of the side-effects generated by mainland China’s urbanization process is “ghost cities”—generally defined as clusters of abandoned buildings or housing structures—but there is a notable lack of studies on the basic characteristics related to this phenomenon, such as size, growth, level, distribution, scale, intensity, pattern and determinants. Through a combination of nighttime satellite data and daytime satellite data as a useful proxy, in this paper, we present the spatial pattern and temporal evolution of China’s ghost cities over the last two decades. Nighttime light’s rate of change in newly built areas is developed based on DMSP/OLS and Normalized Difference Built-up Index to assess a city’s darkness. Results show that the ghost city problem is real, but, at least so far, confined to 22 smaller cities. However, further analysis reveals that nighttime lights change in newly built areas, following an inverted U-curve for big cities representing a reversion from positive to negative values for the trends in recent years. The methodology through the use of the complementary characteristics in time between DMSP/OLS and Landsat data in our study prove to serve as deposing the direct evidences to ascertain and quantify such social-economic phenomenon.

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s Gi∗ statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

Degraded air quality by PM 2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM 2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM 2.5 concentration, little research was focused on the use of remotely sensed nighttime light (NTL) imagery. This study evaluated the merits of using NTL satellite images in predicting ground-level PM 2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM 2.5 concentration and analyze its relationships with AOD, meteorological variables, and NTL data across the New England region. Observed data in 2013 were used to test the constructed GWR models for PM 2.5 prediction. The Vegetation Adjusted NTL Urban Index (VANUI), which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI into NTL to overcome the defects of NTL data, was used as a predictor variable for final PM 2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM 2.5 exposure, especially in urbanized and densely populated areas. VANUI data could obviously improve the performance of GWR for the warm season (GWR model with VANUI performed 17% better than GWR model without NDVI and NTL data and 7.26% better than GWR model without NTL data in terms of RMSE), while its improvements were less obvious for the cold season (GWR model with VANUI performed 3.6% better than the GWR model without NDVI and NTL data and 1.83% better than the GWR model without NTL data in terms of RMSE). Moreover, the spatial distribution of the estimated PM 2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM 2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM 2.5 monitoring and prediction.

We are currently living in the era of big data. The volume of collected or archived geospatial data for land use and land cover (LULC) mapping including remotely sensed satellite imagery and auxiliary geospatial datasets is increasing. Innovative machine learning, deep learning algorithms, and cutting-edge cloud computing have also recently been developed. While new opportunities are provided by these geospatial big data and advanced computer technologies for LULC mapping, challenges also emerge for LULC mapping from using these geospatial big data. This article summarizes the review studies and research progress in remote sensing, machine learning, deep learning, and geospatial big data for LULC mapping since 2015. We identified the opportunities, challenges, and future directions of using geospatial big data for LULC mapping. More research needs to be performed for improved LULC mapping at large scales.