With the increasing volume and active transaction of real estate properties, mass appraisal has been widely adopted in many countries for different purposes, including assessment of property tax. In this paper, 104 papers are selected for the systematic literature review of mass appraisal models and methods from 2000 to 2018. The review focuses on the application trend and classification of mass appraisal and highlights a 3I-trend, namely AI-Based model, GIS-Based model and MIX-Based model. The characteristics of different mass appraisal models are analyzed and compared. Finally, the future trend of mass appraisal based on model perspective is defined as “mass appraisal 2.0”: mass appraisal is the appraisal procedure of model establishment, analysis and test of group of properties as of a given date, combined with artificial intelligence, geo-information systems, and mixed methods, to better model the real estate value of non-spatial and spatial data.

The traditional linear regression model of mass appraisal is increasingly unable to satisfy the standard of mass appraisal with large data volumes, complex housing characteristics and high accuracy requirements. Therefore, it is essential to utilize the inherent spatial-temporal characteristics of properties to build a more effective and accurate model. In this research, we take Beijing’s core area, a typical urban center, as the study area of modeling for the first time. Thousands of real transaction data sets with a time span of 2014, 2016 and 2018 are conducted at the community level (community annual average price). Three different models, including multiple regression analysis (MRA) with ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), are adopted for comparative analysis. The result indicates that the GTWR model, with an adjusted R2 of 0.8192, performs better in the mass appraisal modeling of real estate. The comparison of different models provides a useful benchmark for policy makers regarding the mass appraisal process of urban centers. The finding also highlights the spatial characteristics of price-related parameters in high-density residential areas, providing an efficient evaluation approach for planning, land management, taxation, insurance, finance and other related fields.

Due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geological, fault, and lithology), it is difficult to assess the hazard of landslides at the regional scale quantitatively only considering rainfall condition. Based on detailed landslide inventory and rainfall data in the hilly area in Sichuan province, this study analyzed the effects of both rainfall process and environmental factors on the occurrence of landslides. Through analyzing environmental factors, a landslide susceptibility index (LSI) was calculated using multiple layer perceptron (MLP) model to reflect the regional landslide susceptibility. Further, the characteristics of rainfall process and landslides were examined quantitatively with statistical analysis. Finally, a probability model integrating LSI and rainfall process was constructed using logistical regression analysis to assess the landslide hazard. Validation showed satisfactory results, and the inclusion of LSI effectively improved the accuracy of the landslide hazard assessment: Compared with only considering the rainfall process factors, the accuracy of the landslide prediction model both considering the rainfall process and landslide susceptibility is improved by 3%. These results indicate that an integration of susceptibility index and rainfall process is essential in improving the timeliness and accuracy of regional landslide early warning.

The highway is an important mode of transportation in the Qinghai–Tibet Plateau, and can be regarded as a major contributor to the high-quality and sustainable development of the Qinghai–Tibet Plateau. It is of great significance to explore its spatial distribution and characteristics for understanding the regional and geographical process. Although Qinghai–Tibet Plateau’s highway transportation infrastructure has been experiencing rapid development in recent years, there lacks a systematic examination of the whole Qinghai–Tibet Plateau from the perspective of supportive capacity for its socio-economic activities. This paper applies geospatial analysis methods, such as network analysis, spatial statistics, and weighted overlay, to model the highway transport dominance in the Qinghai–Tibet Plateau in 2015 at the county scale and reveals the basic characteristics of the highway transport dominance’s spatial pattern. The results are mainly of four aspects: 1) there is a significant difference between the east and west of the highway in the Qinghai–Tibet Plateau, showing an irregular circle structure of gradual attenuation from the east to west; 2) at the county scale, the highway transport dominance in the Qinghai–Tibet Plateau shows strong spatial autocorrelation and a certain extent of spatial heterogeneity, presenting a spatial distribution pattern of High–High and Low–Low clustering; 3) the urban locations of Lhasa, Xining and other center cities have obvious spatial constraints on the distribution of highway transport dominance and generally have a logarithmic decline trend; and 4) there are obvious differences in distribution among the three Urban Agglomerations in the Qinghai–Tibet Plateau. Due to the influence of traffic location, topography, construction of national trunk lines, and level of socio-economic development., the traffic conditions of Lan-Xi Urban Agglomeration and Lhasa Urban Agglomeration are better than Kashgar Urban Agglomeration. This study can be used to guide the optimization of the highway network structure and provide a macro decision-making reference for the planning and evaluation of major highway projects in the Qinghai–Tibet Plateau.

As the core element of social-economic development in the Qinghai-Tibet Plateau, transportation dramatically shapes the scale, type, and intensity of human activities. First, this study utilizes night light data and kilometer-grid population data to construct night light development index (NLDI) and to evaluate the human development level at the county scale. Then, based on the complex transportation infrastructure data, the weight assignment method is adopted to create transportation infrastructure influence degree (TIID), which is used to evaluate the location conditions of the counties. Finally, bivariate spatial autocorrelation is utilized to analyze the effect of regional conditions on the county-level human development variation. The results show that (1) NLDI is verified to assess differences in the level of human development among counties in Qinghai-Tibet Plateau and to overcome the difficulties of systematically and integrally obtaining socio-economic statistical data. The pattern of human development level in Qinghai-Tibet Plateau presents a “core-periphery” spatial structure with the transportation network as the axis. (2) On the whole, with the improvement of location conditions influenced by transportation infrastructure, the spatial aggregation of human development level is constantly improving, and the spatial disparity continues to decrease. (3) Locally, four spatial interaction patterns of high/low clustering are recognized and analyzed. It reflects the complexity and spatial heterogeneity between transport infrastructure construction and human development level in the Qinghai-Tibet Plateau.