Humanities and Social Sciences (HSS) are undergoing the transformation of spatialization and quantification. Geo-computation, with geoinformatics (including RS: Remote Sensing; GIS: Geographical Information System; GNSS: Global Navigation Satellite System), provides effective computational and spatialization methods and tools for HSS. Spatial Humanities and Geo-computation for Social Sciences (SH&GSS) is a field coupling geo-computation, and geoinformatics, with HSS. This special issue accepted a set of contributions highlighting recent advances in methodologies and applications of SH&GSS, which are related to sentiment spatial analysis from social media data, emotional change spatial analysis from news data, spatial analysis of social media related to COVID-19, crime spatiotemporal analysis, “double evaluation” for Land Use/Land Cover (LUCC), Specially Protected Natural Areas (SPNA) analysis, editing behavior analysis of Volunteered Geographic Information (VGI), electricity consumption anomaly detection, First and Last Mile Problem (FLMP) of public transport, and spatial interaction network analysis for crude oil trade network. Based on these related researches, we aim to present an overview of SH&GSS, and propose some future research directions for SH&HSS.

The delimitation of urban development boundaries plays an important role in optimizing the nation land space. “Double evaluation” is one of the important means to study and predict the scale of new construction land in the future and to determine the spatial distribution of urban construction land. This study combines the “double evaluation” with the FLUS (Future Land-Use Simulation) model to study the delimitation of the urban development boundary of Yichang. The results show that: ① the “double evaluation” method comprehensively considers the carrying capacity of the resource environmental bear and the suitability of urban development; ② the FLUS model can better couple the “double evaluation” method for Land Use/Land Cover (LULC) suitability evaluation, Land Use/land Cover Change (LUCC) simulation and urban development boundary delineation, and the overall accuracy of the simulation reaches 96%; ③ according to the requirements of relevant national policies, this study divides the urban development boundary of the study area into concentrated construction areas, elastic development areas and special purpose areas. This function-based division can meet the requirements of urban flexible development, ecological protection and urban safety. This research combines the FLUS model, which is widely used in the simulation of LUCC, with the double evaluation method used in China’s new round of land and space planning to obtain the result of the urban development boundary. This result is consistent with the existing plan of the study area.

Based on the official criminal data released by the Tokyo Metropolitan Police Department in 2019, this paper discusses the temporal-spatial distribution of various types of crimes in the special wards of Tokyo. The results show that: ① The times of high and low incidence of different types of crime differ significantly. Although vicious crime and violent crime present no obvious monthly distribution, property crime clearly differs between the first and second half of a calendar year. ② The month before the new year sees a surge in most types of crime. ③ Vicious crime peaks in the hours between night and early morning. Violent crime and property crime correlate positively with the frequency of human interaction and peak in the morning and evening commuting hours. ④ The spatial distribution of crime resembles the concentric circles of the three rings of the special wards of Tokyo, with a central high-incidence area, a center-peripheral low-incidence area, and a marginal high-incidence area. In addition, the center sees more personal crime than the periphery, whereas property crimes show the opposite trend. ⑤ A spatial autocorrelation analysis shows that the special wards of Tokyo may be grouped into the “high-high” and “low-low” agglomeration modes of different types of crime, with marked differences between the various types of crime. The crime can be divided into three types: central agglomeration, double central agglomeration, and decentralized agglomeration.

Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity consumption by customers, safe operation of power grids, and sustainable development of cities. However, current abnormal electricity consumption detection models do not consider the time dependence of time-series data and rely on a large number of training samples, and no study has analyzed the spatial distribution and influencing factors of abnormal electricity consumption in urban areas. In this study, we use the Seasonal-Trend decomposition procedure based on Loess (STL) based time series decomposition and outlier detection to detect abnormal electricity consumption in the central city of Pingxiang, and analyze the relationship between spatial variation and urban functions through Geodetector. The results show that the degree of abnormal electricity consumption in urban areas is related to geographic location and has spatial heterogeneity, and the abnormal electricity users are mainly located in areas with highly mixed residential, commercial and entertainment functions in the city. The results obtained from this study can provide a reference basis and a theoretical foundation for the detection of abnormal electricity consumption by users and the arming of electricity theft devices in the power grid.

In response to the COVID-19, social media big data has played an important role in epidemic warning, tracking the source of infection, and public opinion monitoring, providing strong technical support for China’s epidemic prevention and control work. The paper used Sina Weibo posts related to COVID-19 hashtags as the data source, and built a BERT-CNN deep learning model to perform fine-grained and high-precision topic classificationon massive social media posts. Taking Shenzhen as a region of interest, we mined the “epidemic data bulletin” and “daily life impact” posts during the epidemic for spatial analysis. The results show that the confirmed communities and designated hospitals in Shenzhen as a whole present the characteristics of “sparse east and dense west”, and there is a strong positive spatial correlation between the number of confirmed cases and social media response. Specifically, Nanshan District, Futian District and Luohu District have more confirmed cases due to large population movements and dense transportation networks, and social media has responded more violently, and people’s lives have been greatly affected. However, Yantian District, Pingshan District and Dapeng New District showed opposite characteristics. The case study results further show that using deep learning methods to mine text information in social media is scientifically feasible for improving situational awareness and decision support during the COVID-19.

It is recognized that a city with a livable environment can bring happiness to residents. In this study, we explored the social media users’ emotional states in their current living spaces and found out the relationship between the social media users’ emotions and urban livability. We adopt six urban livability indicators (including education, medical services, public facilities, leisure places, employment, and transportation) to construct city livable indices. Also, the Analytic Hierarchy Process (AHP) spatial statistic method is applied to identify and analyze the different habitable regions of Wuhan City. In terms of citizen’s emotion analysis, we use Long Short-Term Memory (LSTM) neural network to analyze the Weibo text and obtain the Weibo users’sentiment scores. The correlation analysis of residents’ emotions and city livability results shows a positive correlation between the livable city areas (i.e., the area with higher livable ranking indices) and Weibo users’ sentiment scores (with a Pearson correlation coefficient of 0.881 and P-Value of 0.004). In other words, people who post Weibo in high livability areas of Wuhan express more positive emotional states. Still, emotion distribution varies in different regions, which is mainly caused by people’s distribution and the diversity of the city’s functional areas.

Based on the theories and methods of complex network, crude oil trade flows between countries along the Belt and Road (B&R, hereafter) are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges. The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy. This paper researches and discusses the construction, statistical analysis, top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences (GCSS) and spatial interaction. Firstly, evolutions of out-degree, in-degree, out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed. Secondly, the top network method is used to explore the evolution characteristics of hierarchical structures. And finally, the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function. The analysis results show that Russia has the largest out-degree and out-strength, and China has the largest in-degree and in-strength. The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90% of the total trade volume of the crude oil trade network, and the proportion remains relatively stable. However, the stability of the network showed strong fluctuations in 2009, 2012 and 2014, which may be closely related to major international events in these years, which could furtherly be used to build a correlation model between network volatility and major events. This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.

To tackle environmental challenges and in particular, the problems associated with the high demand for forests and resources, the consequence of which is the degradation of ecological systems, Russia has developed the biggest network of specially protected natural areas in the world. Although the scale of SPNA may look impressive, a recent comprehensive study of the period from 2001 to 2018 brought the spotlight on protected areas in Russia experiencing significant tree loss annually. Since Russia is confronting unprecedented fires that break records every year, the problem of preserving rare animal and plant species, their habitats, and natural ecosystems is acute. This paper is intended to assess the extent and effectiveness of SPNAs with regard to wilderness conservation in the Russian Federation based on the latest data; the main focus being on the period of 2014—2019, before and after the launch of the environmental safety strategies, along with additional analysis of available data for the following year of 2020. With due consideration of unique geographical, economic, and socio-cultural circumstances, as well as historical background and legislative reality of SPNAs in Russia, we have conducted a statistical analysis of data from the official annual reports from the Federal State Statistics Service on SPNAs by territories and significance, expenditures on maintenance and ecotourism, followed by RGB analysis of satellite imagery via means of GIS software for visualization of obtained data and further analytics. Despite the growth of the SPNA network in Russia, resulting in an astonishing 2402Mha in 2020, an estimated 4Mha of tree loss accounted for SPNAs over the period of 2014—2019, and 134Mha in 2020 alone. Our findings indicate a need for the application of a comprehensive GIS approach for further development and effective management of the SPNA network in Russia. Furthermore, the results include recommendations on legislative changes, engagement of locals in SPNA protection, and popularization of ecotourism, all of which can be valuable for policymakers and SPNA development.

OpenStreetMap has a large number of volunteers. There is a hypothesis that volunteers with different cultural backgrounds may have different editing behaviors when contributing to OSM. It may be strongly related to data quality and data reliability on OSM. As for the heterogeneity and the reliability of OSM data, previous research usually focuses on the geometric accuracy, spatial location accuracy and semantic integrity of OSM data, while few researchers have analyzed these problems from the perspective of editing behavior. On the grounds of relationship between mapping motivation and editing behavior, the dispersion of editing trajectory and clockwise direction index are proposed in the paper to explore whether the volunteers are sufficiently motivated and knowledgeable. In the experiments, the historical OSM data of four countries suggested that developed countries have lower trajectory dispersion. The lower degree of trajectory dispersion reflects the higher concentration and professionalism of volunteers. A high degree of drawing direction consistency shows volunteers who mapped French data were natives with local knowledge. From this point of view, this paper verifies that volunteer editing behavior is an effective method to analyze data quality heterogeneity and data reliability.

Spatial-temporal analysis of emotions in society has become popular in many studies integrating geography with the humanities, and has shown its influence on social sensing and geo-computation for social sciences. Emotions in society are often volatile, irrational, and vulnerable to the social environment. A critical challenge is to analyze changes in long-term and large-scale emotions in society. In this paper, we propose exploiting this challenge by using spatial-temporal analysis. After extracting emotional, temporal, and spatial information, a spatial standardization approach based on adataset of administrative district changes addresses the problem of Chinese toponym changes. Finally, over 1.7 million news data from the People’s Daily from 1956 to 2014 were collected to explore the changes, spatial distribution, and driving factors of emotions in society using spatial-temporal analysis. The experimental results found that the spatial-temporal analysis of emotions in society in the news is consistent with the results of related sociological research.

With continual population growth in the densely populated cities, e.g., Singapore, traffic congestion is the main transportation problem. As such, Public Transport is deemed as the most sustainable mode of transport. However, pubic transport is often plagued with the First and Last Mile Problem (FLMP), reducing its appeal especially in the densely populated city areas. The FLMP leads to a wide range of complications, in particular, human congestion in public transport. In this research, after a comprehensive discussion of FLMP problem in densely populated city areas, a case study in Singapore, i.e., the Kent Ridge Campus of National University of Singapore (KRC-NUS), has been conducted based on the survey and spatio-temporal analyses. In addition to the investigation of FLMP status in the case study area, a new campus shuttle bus route from an alternative MRT station (Haw Par Villa Station) has also been proposed based on the Tabu Search to alleviate the human congestion and current FLMP status for KRC-NUS. This research not only has successfully addressed the FLMP in the case study area, but also can be a good reference for other areas in densely populated cities to help mitigate FLMPs.

Remote sensing provides key inputs to a wide range of models and methods developed for quantifying forest carbon. In particular, carbon inventory methods recommended by IPCC require biomass data and a suite of forest disturbance products. Significant progress has been made in deriving these products by leveraging publicly available remote sensing assets, including observations acquired by the legendary Landsat mission and new systems launched within the past decade, including Sentinel-2, Sentinel-1, GEDI, and ICESAT-2. With the L-band NISAR and P-band BIOMASS missions to be launched in 2023, the Earth’s land surfaces will be imaged by optical and multi-band (including C-, L-, and P-bands) radar systems that can provide global, sub-weekly observations at sub-hectare spatial resolutions for public use. Fine scale products derived from these observations will be crucial for developing monitoring, reporting, and verification (MRV) capabilities needed to support carbon trade, REDD+, and other market-driven tools aimed at achieving climate mitigation goals through forest management at all levels. Following a brief discussion of the roles of forests in the global carbon cycle and the wide range of models and methods available for evaluating forest carbon dynamics, this paper provides an overview of recent progress and forthcoming opportunities in using remote sensing to map forest structure and biomass, detect forest disturbances, determine disturbance attribution, quantify disturbance intensity, and estimate harvested timber volume. Advances in these research areas require large quantities of well—distributed reference data to calibrate remote sensing algorithms and to validate the derived products. In addition, two of the forest carbon pools-dead organic matter and soil carbon—are difficult to monitor using modern remote sensing capabilities. Carefully designed inventory programs are needed to collect the required reference data as well as the data needed to estimate dead organic matter and soil carbon.

Nowadays Surveying and Mapping (S&M) production and services are facing some serious challenges such as real-timization of data acquisition, automation of information processing, and intellectualization of service applications. The main reason is that current digitalized S&M technologies, which involve complex algorithms and models as the core, are incapable of completely describing and representing the diverse, multi-dimensional and dynamic real world, as well as addressing high-dimensional and nonlinear spatial problems using simple algorithms and models. In order to address these challenges, it is necessary to explore the use of natural intelligence in S&M, and to develop intelligentized S&M technologies, which are knowledge-guided and algorithm-based. This paper first discusses the basic concepts and ideas of intelligentized S&M, and then analyzes and defines its fundamental issues in the analysis and modeling of natural intelligence in S&M, the construction and realization of hybrid intelligent computing paradigm, and the mechanism and path of empowering production. Further research directions are then proposed in the four areas, including knowledge systems, technologies and methodologies, application systems, and instruments and equipments of intelligentized S&M. Finally, some institutional issues related to promoting scientific research and engineering applications in this area are discussed.