In the era of big data, advances in relevant technologies are profoundly impacting the field of real estate appraisal. Many scholars regard the integration of big data technology as an inevitable future trend in the real estate appraisal industry. In this paper, we summarize 124 studies investigating the use of big data technology to optimize real estate appraisal through the hedonic price model (HPM). We also list a variety of big data resources and key methods widely used in the real estate appraisal field. On this basis, the development of real estate appraisal moving forward is analyzed. The results obtained in the current studies are as follows: First, the big data resources currently applied to real estate appraisal include more than a dozen big data types from three data sources; the internet, remote sensing, and the Internet of things (IoT). Additionally, it was determined that web crawler technology represents the most important data acquisition method. Second, methods such as data pre-processing, spatial modeling, Geographic information system (GIS) spatial analysis, and the evolving machine learning methods with higher valuation accuracy were successfully introduced into the HPM due to the features of real estate big data. Finally, although the application of big data has greatly expanded the amount of available data and feature dimensions, this has caused a new problem: uneven data quality. Uneven data quality can reduce the accuracy of appraisal results, and, to date, insufficient attention has been paid to this issue. Future research should pay greater attention to the data integration of multi-source big data and absorb the applications developed in other disciplines. It is also important to combine various methods to form a new united evaluation model based on taking advantage of, and avoiding shortcomings to compensate for, the mechanism defects of a single model.

Due to limited land resources, it is necessary to balance urban economic development and efficient land use. Clarifying the relationship between the two is crucial to improving both economic efficiency and land use efficiency. Considering the undesirable output of urban land use, this paper adopts a super efficiency SBM model to quantify the urban land use efficiency (ULUE) of the Beijing–Tianjin–Hebei (BTH) region from 1999 to 2019, and analyzes the relationship between ULUE and economic development level (EDL) by combining the Tapio model and the environmental Kuznets curve (EKC) model. The results show the following: (1) During the study period, the ULUE showed a fluctuating upward trend on the temporal scale, with the lowest and highest inflection points occurring in 2002 and 2018, respectively, and a distribution pattern of “high in the southeast and low in the northwest” on the spatial scale. (2) The decoupling relationship between ULUE and EDL showed repeated fluctuations between decoupling and coupling states on the temporal scale, but the overall showed a transition trend from decoupling state to coupling state. On the spatial scale, from north to south, there were a strong decoupling state (SDS), weak decoupling state (WDS), strong decoupling state (SDS), and weak decoupling state (WDS) in order, showing a regular interval repetition distribution pattern. (3) The relationship between ULUE and EDL showed an EKC “U-shaped” curve, that is, ULUE decreases first and then increases with the increases in EDL. The results of this study can provide a reference for the coordinated and sustainable development of the BTH region.

The Han River Basin, a critical water conservation and ecological barrier in Hubei Province, is intricately associated with the United Nations Sustainable Development Goals (SDGs). Research results show that vegetation cover changes are affected by multiple factors, and understanding the influences of climate change and human activities on vegetation is imperative for achieving sustainable development in the basin. Through quantitative assessment of vegetation changes in diverse landform regions, implementing adaptive ecological construction and environmental protection will foster the sustainable development of ecological civilization in the Han River Basin. This study utilizes MODIS13Q1 data and employs diverse analytical methods to investigate the characteristics of vegetation change and the interrelationships between climate change, meteorological factors, and vegetation cover in various geomorphological areas of the Han River Basin from 2000 to 2020. The results showed that (1) throughout the entire study period, the NDVI of the six types of geomorphological divisions in the Han River Basin exhibited a fluctuating upward trend, with the changes in the low-altitude hilly geomorphic regions being particularly noteworthy. (2) Within the study area, approximately 92.67% of vegetation coverage displayed an increasing trend, while 7.33% showed degradation, predominantly in plains and platforms. Notably, the area of continuous improvement (31.16%) outweighed the area of continuous degradation (3.05%), with low and middle-relief mountain areas demonstrating the most robust growth and sustainability. (3) Human agriculture activities and urbanization processes have emerged as the primary driving force behind vegetation changes in the Han River Basin. The responses of vegetation to climate change and human activities exhibited significant variations across diverse geomorphological regions. In areas characterized by vegetation improvement, the contribution rate of human activities to NDVI changes in different vegetation types surpassed 70%, with plain areas displaying the highest contribution rate at a remarkable 90%. In contrast, the plain and platform regions of the vegetation degradation area were significantly influenced by climate change. In future watershed ecological environment management, it is essential to not only recognize the dominant role of human activities in promoting the growth of mountain vegetation NDVI but also address the impact of climate change on the degradation of vegetation NDVI in plains and platforms. A comprehensive understanding of these factors is crucial for devising effective strategies to ensure sustainable development and ecological balance in the Han River Basin.

The Hanjiang River Basin (HJRB) is an important water conservation and ecological barrier area for the South–North Water Transfer Central Project. The quantitative analysis of regional differences in vegetation changes and their main drivers is important for the monitoring of the ecological environment of the basin and formulation of ecological protection measures. Based on MODIS13Q1 data from 2000 to 2020, spatiotemporal variation characteristics of vegetation in the HJRB were analyzed using Theil–Sen + Mann–Kendall, the Hurst index, and correlation analysis. Then, we detected the drivers using an optimal parameter geographic detector. The results showed that from 2000 to 2020, the average NDVI value increased from 0.651 to 0.737, with a spatial distribution pattern of “high in the northwest and low in the southeast”, and 88.68% of the study area showed an increase in vegetation cover, while 5.80% showed a significant degradation. The positive persistence of future vegetation changes is stronger than the negative. It may show a slowdown or degradation trend, among which the vegetation restoration along the Han River and urbanized areas need to be strengthened. The factor detector indicated that the main factors influencing vegetation change were topography and climate, for which the most influential variables, respectively, were elevation (0.1979), landform (0.1720), slope (0.1647), and soil type (0.1094), with weaker influence from human activity factors. The interaction test results showed that the interaction of various geographic factors enhanced the explanatory power of vegetation changes and showed mainly nonlinear and two-factor enhancements. The dominant factor varies between sub-basins; for example, the interaction between wind speed and land use conversion was the dominant factor in the middle reaches of the HJRB; the dominant factor in the lower reaches of the HJRB was expressed as the interaction between land use conversion and temperature. Finally, the effects of the range or category of different drivers on vegetation growth were systematically analyzed. The results of the study contribute to the understanding of the dynamic changes of vegetation based on a comprehensive consideration of the interaction of topography, climate, and human activities, taking into account the totality and variability of the geographical environment, and provide a reference for the ecological restoration and rational use of vegetation resources in the HJRB.

Mixed land use has the advantages of promoting the economic and intensive utilization of land and improving the efficiency of land use, which can help alleviate the current urban problems and promote the sustainable development of cities. Existing studies have usually used quantitative indicators to reflect complex and diverse mixed land use situations, and the conclusions obtained usually cannot provide a basis for functional selection in mixed land use practices. Therefore, this study took Shenzhen as the study area to explore whether there are differences in the urban vibrancy enhancement among different mixed land use types. First, the block-scale mixed land use dataset of the study area was constructed. Second, the spatial distribution characteristics of the main functional types and urban vibrancy in the study area were explored. Finally, the impact of mixed land use types on urban vibrancy was explored by using a multiple linear regression model and setting land use type as the dummy variable. The results show that the number of mixed-function blocks in Shenzhen is relatively small, and the mixed land use degree still needs to be improved. Among the 12 main land use types in the study area, those containing industrial land are usually clustered in the northern industrial area of Shenzhen, those containing public or commercial service land are usually clustered in the city center, and those containing residential land are widely distributed in the study area. From the perspective of urban vibrancy, there is a phenomenon of “jobs–housing mismatch” in Shenzhen, as well as a problem of low urban vibrancy in the peripheral areas of the city. In addition, the urban vibrancy intensity of mixed land use types including residential or commercial land is higher, such as “administration+residential”, “residential+commercial”, “industrial+residential+commercial”, and “administration+residential+commercial” land, which includes residential or commercial land, is stronger, while. However, the urban vibrancy stability of mixed land use types including industrial land is higher, such as “industrial+residential” and “industrial+administration” land. The results of this study can provide a basis for future mixed land use practices in terms of land use type selection. For the urban central areas and subcenters in urban peripheral areas, mixed land use types such as “administration+residential”, “residential+commercial”, and “administration+residential+commercial” can be selected to enhance the urban vibrancy stability of the area. For industrial parks in urban peripheral areas, mixed land use types such as “industrial+residential”, “industrial+commercial”, “industrial+administration+residential”, and “administration+residential+commercial” can be selected to enhance the urban vibrancy intensity of the area.

To coordinate the economy and environment in mining cities, it is critical to understand the ecological effects of land use/cover change (LUCC). Therefore, we selected a typical mining city to analyze LUCC-driven ecosystem service changes. In this study, we first used the equivalent factor method to calculate the ecosystem services valuation (ESV) in Wu’an and verified the rationality of the ESV coefficient through the sensitivity index. Secondly, ArcGIS was used to analyze the spatial change of ecosystem service value and explore the reasons for the change. Finally, the spatial autocorrelation index was calculated to analyze the spatial aggregation characteristics of ESV. The results showed that (1) between 2009 and 2018, the total value of ecosystem services decreased by USD 7.41 million, mainly due to the conversion of cropland to construction land. (2) The individual ecosystem services that contributed the most were waste disposal, water conservation, and soil conservation. The pollution caused by the development of mining has reduced the value of the waste disposal function, and the reduction in water body area has been the main factor limiting the water conservation function. (3) The areas with the most significant changes in ecosystem services were concentrated in the east-north direction, where mining resources were widely distributed, and near the central city. Furthermore, there were relatively small losses in the north-west direction, which was related to the protection of ecological resources influenced by topographical factors and less anthropogenic disturbance. (4) The value of ecosystem services and their dynamics exhibited obvious spatial autocorrelation and high-low value (HL) clustering in Wu’an. The high-value and low-value areas dissolved and penetrated each other, and the low-high value (LH) clustering and HL clustering were scattered. The high-value areas were mostly shown in strips, as they were the main locations of water bodies. This study is crucial for mining cities to maintain spatial stability and sustainable development, and the results provide a scientific basis for land use management decision makers to regulate land more precisely.