空间人文与社会地理计算专刊 | 《测绘学报（英文版）》2022年第2期发布

🔷Background | 专刊背景

21世纪是时空大数据的时代，随着遥感技术、传感网技术、移动通讯技术等技术的快速发展，遥感（Remote Sensing）已经从传统的对地观测（Earth observation）发展到现在的既对地观测，也对人观测（Human observation），发展成既包括传统遥感，也包括社会感知（Social Sensing）的大遥感时代。遥感和地理信息科学技术的研究领域已经从传统的自然环境领域，发展到人文社会科学领域。同时，人文社会科学领域的研究者也在积极拥抱时空大数据分析技术，发展了计算社会科学等新方向。遥感地理信息科学与人文社会科学的跨学科交叉融合促进了一些新型学科交叉方向的蓬勃发展，如社会地理计算、文学GIS、历史GIS、艺术GIS、遥感经济学、遥感新闻学、遥感考古学、空间社会网络、犯罪地理学、健康地理与公共卫生学、人类行为动力学等，以及GIS与国际关系学、GIS与哲学、GIS与管理学等。 

为了对空间人文与社会地理计算这一新型学科交叉方向的研究进行全面梳理，决定在《测绘学报（英文版）》（Journal of Geodesy and Geoinformation Science,  http://jggs.sinomaps.com）组织一个关于空间人文与社会地理计算的英文专辑，将相关学者的研究进行集中出版，以进一步促进该方向的发展。

🔷 Contents | 专刊目录

🔷 Overview | 专刊概览

Spatial Humanities and Geo-computation for Social Sciences: Advances and Applications

Kun QIN,1, Hui LIN2, Yang YUE3, Feng ZHANG4, Jianya GONG1

1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

2. School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China

3. School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China

4. School of Earth Sciences, Zhejiang University, Hangzhou 310028, China

Abstract

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.

Keywords：

Humanities and Social Sciences(HSS); Spatial Humanities and Geo-computation for Social Sciences(SH&GSS); sentiment spatial analysis; spatial analysis for social media; crime spatiotemporal analysis; editing behavior analysis; spatial interaction network analysis

Cite this article

Kun QIN, Hui LIN, Yang YUE, Feng ZHANG, Jianya GONG. Spatial Humanities and Geo-computation for Social Sciences:Advances and Applications[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(2): 1-6 doi:10.11947/j.JGGS.2022.0201

1 Introduction

The 21st century is the era of spatiotemporal big data. With the rapid development of remote sensing, sensor network, mobile communication and related technologies, remote sensing has shifted from focusing on traditional earth observation to integrating human observation^[1], thus giving birth to the broad remote sensing era that combines remote sensing and social sensing in the 21st Century^[2-3]. The scholars in the fields of remote sensing and geographic information science have developed from studying traditional natural environmental science to humanities and social sciences^[4-5]. The scholars in the field of humanities and social sciences have also adopted spatiotemporal big data analytic approaches^[6], and have proposed new directions such as computational social sciences^[7]. The interdisciplinary research between remote sensing, geographic information science and humanities and social science has promoted the vigorous development of some new interdisciplinary directions, such as Geo-computation for Social Sciences^[1,8], Literature Integrated GIS^[6], History Integrated GIS^[9-10], Art Integrated GIS, Remote Sensing Economics^[11⇓-13], Remote Sensing Journalism^[14], Remote Sensing Archaeology^[15], Spatial Social Network^[16], Geography for Crime^[17-18], Health and Geography of Public Health^[19-20], Human Dynamics^[21], International Relations and GIS^[22], GIS and Philosophy, GIS and Management Sciences^[23], and so on.

Spatial Humanities and Geo-computation for Social Sciences ( SH&GSS) is an interdisciplinary field coupling geo-computation, and geoinformatics, with HSS. Humanities are the disciplines about the knowledge of the human heart and feeling, including philosophy, history, literature, linguistics, journalism, art, and so on. Social sciences are the disciplines about the research of various social phenomena, including economy, politics, sociology, law, management, and so on. Geo-computation was originally introduced in the first international conference on “Geo-computation”, hosted by the School of Geography at the University of Leeds in 1996^[24]. Geo-computation provides computational methods for HSS, and geoinformatics provides spatial analysis methods and geographic visualization methods for HSS.

SH&GSS is the combination of two related similar branches including Spatially Integrated Humanities and Social Sciences (SIHSS)^[4] and geo-computation for social science^[1]. In 1999, the Center for Spatially Integrated Social Science was established in the National Center for Geographic Information and Analysis (NCGIA). The center aimed to promote the applications of geographic spatial analysis methods into social sciences. In 2006, a review paper titled “Research Progress of Spatially Integrated Humanities ad Social Sciences” was published^[4], which opened a new research direction for spatially integrated humanities and social sciences. Until now, 11 annual conferences of SIHSS have been held, which attracted numerous scholars to take part in the research of the field. Another related research branch is Geo-Computation for Social Sciencs (GCSS), which is originally the establishment of the Geo-computation Center for Social Sciences at Wuhan University on January 8th, 2018. Geo-computation for social science is a new discipline that employs RS earth observations and is driven by the spatiotemporal big data that reflects surface features and human activities. It senses, analyzes, and mines categories and intensities of human activities and their influences on natural and social environments in multiple spatial and temporal dimensions^[1]. SIHSS emphasizes “spatially integrated” which means providing spatially integrated methods or platforms for HSS. GCSS emphasizes “geo-computation” which means providing geo-computation and spatial analysis methods for HSS. We combine these two branches into SH&HSS, providing spatially integrated methods and geo-computation methods for HSS.

This special issue aims to comprehensively sort out the interdisciplinary researches on SH&GSS. The 10 accepted papers are related to the recent advances in methodologies and applications of SH&GSS. Please refer to the followed papers for greater details.

2 Advanced Techniques

Geo-computation, with geoinformatics, expand the applications into the fields of humanities and social sciences, and provide spatialization, geo-visualization, and quantification methods for HSS. It is a key method to introduce spatial thinking, and spatial concepts into the research of humanities and social sciences. It has been gradually accepted by scholars of HSS to implement geospatial thinking, and use spatial econometrics approaches^[25]. Virtual Reality (VR) techniques can be introduced into SHH to analyze the behavior of a human in a virtual geographic environment. VR can be used to rebuild or deduce the historical scenarios^[26].

Geosimulaiton models can be utilized to simulate the urban growth. A Cellular Automaton (CA) and Multi-Agent Systems (MAS) have been particularly popular. “Double evaluation” is one of the import means to study and predict the scale of new construction land in the future and to determine the spatial distribution of urban construction land^[27]. The combination of “double evaluation” and the Future Land-Use Simulation (FLUS) model can be applied into the application of computer simulation under geographical conditions in China^[28].

Spatial statistical analysis methods are popular applications into humanities and social sciences. For example, spatial autocorrelation analysis methods can be used in crime analysis to measure the spatial aggregation of different types of crime^[29]. Geographically Weighted Regression (GWR) provides spatially varying coefficient estimates via location-specific weighted regression model calibrations, to explore spatial heterogeneities or non-stationarities, quantitatively^[30]. It has been widely used in a number of fields, including some fields of social sciences, for example, house price analysis^[31], ecological analysis, and so on. Geodetector is a new statistical method to detect spatial stratified heterogeneity and reveal the driving factors behind it^[32]. For example, Liang et al.^[33] utilized Geodetector to analyze the degree of influence of each type of PoI (Point of Interest) enrichment factor on the spatial layout of urban electricity anomalies.

Deep learning models are not only used in physical sciences, but also are utilized in social sciences. For example, Zhang and Qi^[34]built a Bidirectional Encoder Representations from Transformers-Convolutional Neural Network ( BERT-CNN) deep learning model to perform fine-grained and high-precision topic classification on massive social media posts And Long Short-Term Memory (LSTM) neural network is utilized to analyze the Weibo text and obtain the Weibo users’ sentiment scores^[35].

Time series analysis and outlier detection are often used to analyze the problems in the field of SH&GSS. For example, Liang et al.^[33] utilized 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, Jiangxi province, China.

Spatial interaction network analysis methods can be utilized to analyze directional flow networks (people flow, commodity flow, capital flow, information flow, etc.) which are embedded in geographic space. Spatial interaction networks reflect the interrelationships between objects in geographic space with their spatial interaction characteristics and it is one of the core technologies of GCSS to analyze social science problems. For example, Wang et al.^[36] utilized spatial interaction network analysis to analyze crude oil relations.

3 Emerging Applications

The 10 accepted papers in this special issue represented some emerging applications of SH&GSS, such as topics relating to the social aspects of physical space, energy issues, and spatially-embedded emotion and behavior.

(1) Social aspects of physical space

The delimitation of urban development boundaries plays an important role in optimizing the National Land Space. Jiang and Xiao^[27] combined the “double evaluation” with the Future Land-Use Simulation (FLUS ) model to study the delimitation of the urban development boundary of Yichang, Hubei Province, China. 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. The degradation of ecological systems is an important problem to tackle environmental challenges.

Creating a network of Special Protected Natural Areas (SPNAs) is an effective method to protect ecological systems. Focusing on this problem, Sergeeva and Lin^[37] studied the extent and effectiveness of protected areas in the Russian Federation. Russia has developed the biggest network of specially protected natural areas in the world. The results of the study indicate a need for the application of a comprehensive GIS approach for further development and effective management of the SPNA network in Russia.

The First and Last Mile Problem (FLMP) is a key problem of public transport. Ang and Cao^[38] proposed a method to solve the FLMP problem in densely populated city areas, and gave a case study in Singapore. The paper addressed the FLMP in the study area, and gave a reference for other areas in densely populated cities to help mitigate FLMPs.

(2) Energy issues

Electricity consumption is a means of social sensing. It can be used to sense human activities. Liang et al.^[33] estimated the spatial variation of electricity consumption anomalies and the influencing factors. The paper utilized the STL time series decomposition and outlier detection to detect abnormal electricity consumption in the central city of Pingxiang, and analyzed the relationship between spatial variation and urban functions through the Geodetector.

Crude oil, as one of the main energy sources, is an important and irreplaceable import commodity, which plays a vital in national economic development and international energy security. Wang et al.^[36] utilized spatial interaction network analysis methods to research the crude oil trade relations between countries along the Belt and Road(B&R). This paper examined and discussed 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 GCSS and spatial interaction.

(3) Spatially-embedded emotion and behavior

Emotion spatial analysis is an important method of SH&GSS, which utilizes spatial analysis methods to analyze the emotion states in the spatial environment. Chen et al.^[35] selected six urban livability indicators (including education, medical services, public facilities, leisure places, employment, and transportation) to construct city livable indices, and applied the Analytic Hierarchy Process (AHP) spatial statistic method to identify and analyze the different habitable regions of Wuhan City, China. Huai et al.^[39] researched the method of spatiotemporal analysis of emotions in society in news. The paper collected over 1.7 million news data from the People’s Daily from 1956 to 2014, and explored the changes, spatial distribution, and driving factors of emotions in society using spatiotemporal analysis.

OpenStreetMap (OSM) has a large number of volunteers and has developed into one of the largest open sources of Volunteer Geographic Information (VGI) projects in recent years. Volunteers with different cultural backgrounds may have different editing behaviors when contributing to OSM, which may strongly affect data quality and data reliability. Zhao and Fan^[40] explored the patterns of editing behavior on OpenStreetMap, and verified that volunteer editing behavior is an effective method to analyze data quality heterogeneity and data reliability.

Crime and health geography are important research branches of SH&GSS. Liu et al.^[29] discussed the spatiotemporal distribution of various types of crimes in the special wards of Tokyo, which is based on the official criminal data released by the Tokyo Metropolitan Police Department in 2019. Zhang and Qi^[34] took COVID-19 as the case study, and used the BERT-CNN model to analyze social media text to achieve situational awareness.

4 Outlooks

Humanities and social sciences need spatialization and geo-visualization techniques, and geoinformatics needs to expand its application from physical sciences to social sciences. Remote Sensing Science is undergoing a transition from strictly earth observation to the observation of human activities^[1]. Under these contexts, SH&GSS is proposed to promote these researches. Based on the 10 accepted papers in this special issue and related researches, this paper reviewed the advanced techniques and emerging applications of SH&GSS. SH&GSS is an interesting interdiscipline among geo-computation, geoinformatics, and humanities and social sciences. We hope more scholars would take part in the research of SH&GSS. In the future, the following research directions can be considered, including: ① putting forward more effective spatialization and geo-visualization approaches for HSS; ② providing some online toolkits of spatial analysis and geo-visualization for HSS users; ③ developing online software platforms for HSS, for example, historical GIS platform, art GIS platform, literature GIS platform, etc; ④ education and training. Open more credit courses or training courses about SH&GSS in universities, and attract more students or your scholars to take part in the research of this field.

Acknowledgements: Guest editors would like to take this opportunity to thank all authors, editors, reviewers, and supporters for the hard work and dedication that made this special issue possible.

References 

初审：段鹏丽