GSIS专辑精选-社会地理计算

Original 编辑部地球空间信息科学学报GSIS 2022-07-17

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社会地理计算 是社会人文和计算技术两者融合的新兴研究方向，主要通过对时空大数据分析与挖掘等技术，对与社会发展相关的交通、政治、经济、文化等重大问题，进行跨学科研究，为决策提供支持服务，形成典型应用，以期解决具有社会矛盾突出的综合性、复杂性社会问题。

为此，《地球空间信息科学学报》（Geo-Spatial Information Science，GSIS）推出了“社会地理计算”（Geocomputation for Social Sciences）专辑，武汉大学龚健雅院士，美国科学院院士Luc Anselin教授，武汉大学朱欣焰教授，美国密歇根大学Shuming Bao教授，武汉大学邵远征副研究员为专辑特邀客座编辑。

本期专辑对目前世界各地基于社交媒体、人群流动等数据进行社会性分析的进展进行了较为全面的报道。点击页尾“阅读全文”或扫描下方二维码可免费阅读、下载本期专辑全部文章。

Reshaping the urban hierarchy: patterns of information diffusion on social media

重塑城市层次：社交媒体信息传播模式

Jiue-An Yang, Ming-Hsiang Tsou, Krzysztof Janowicz, Keith C. Clarke & Piotr Jankowski

文章简介

信息的空间扩散是一个受人际交往流动支配的过程。互联网，尤其是社交媒体平台的出现重塑了这一过程。

此前的研究主要关注在线社交网络如何促进信息传播。了解这些过程有助于设计方法，最大限度地或控制信息的范围，甚至确定即将发生的事件和社会运动。然而，网络空间的活动仍然局限于物理位置，这种地理联系往往被忽视。

在本文中，我们关注的是地理区域而不是个人，并研究区域的底层层次结构与他们对信息的反应之间的关系。我们调查了美国前30个人口稠密的城市和大都市地区，并从这些地区检索了与两个选定主题相关的Twitter数据，即2015年尼泊尔地震和针对巴黎《查理周刊》办公室遇袭的JesuisCharlie标签。

我们利用多种统计方法和三个城市分类法分析了它们的反应区域间的相似性。我们的研究结果表明，信息的扩散受到城市区域等级的影响，当人口稠密的区域处于城市等级的同一水平时，Twitter的反应更为相似。

The spatial diffusion of information is a process governed by the flow of interpersonal communication. The emergence of the Internet and especially social media platforms has reshaped this process and previous research has studied how online social networks contribute to the diffusion of information.

Understanding such processes can help devise methods to maximize or control the reach of information or even identify upcoming events and social movements.

Yet activities in cyberspace are still confined to physical locations and this geographic connection tends to be overlooked. In this research, we focus on geographic regions instead of individuals and study how the underlying hierarchical structure of regions relates to their response to the information.

We examined the top 30 populated cities and metropolitan areas in the U.S. and retrieved Twitter data related to two selected topics from these regions, the 2015 Nepal Earthquake and the #JesuisCharlie hashtag in response to the Paris attacks on the Charlie Hebdo offices.

We analyzed the similarity among regions of their response using multiple statistical methods and three urban classifications.

Our results indicate that the diffusion of information is impacted by the hierarchy of urban regions and that the Twitter responses act more similar when the populated regions are positioned at the same level in the urban hierarchy.

Measuring spatio-temporal autocorrelation in time series data of collective human mobility

人类集体流动时间序列数据的时空自相关测量

Yong Gao, Jing Cheng, Haohan Meng & Yu Liu

文章简介

关于人类流动性的海量时空大数据已经越来越多。从这些数据中揭示潜在的动态模式对于理解人们的行为和城市部署至关重要。时空自相关分析是识别数据在空间和时间上分布的一种探索性方法。

目前应用最为广泛的空间自相关测量方法，如Moran的I和空间关联的局部指标（LISA），仅适用于静态数据，对人类活动的时空大数据无能为力。因此，我们提出了一种通过扩展Moran的I来测量时间序列数据的空间自相关的新方法。然后将该方法应用于北京市出租车出行数据，揭示了人群群体流动的空间格局。

结果表明，五环内存在较强的正时空自相关，六环路附近存在微弱的负时空自相关，道路之间几乎没有时空自相关。出租车出行的局部空间格局也得到了认可。该方法有助于从时空大数据中发现潜在的模式，以了解人类的流动性。

Massive spatio-temporal big data about human mobility have become increasingly available. Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.

Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time. The most widely used spatial autocorrelation measurements, such as Moran’s I and local indicators of spatial association (LISA), only apply to static data, so are powerless to spatio-temporal big data about human mobility.

Thus, we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data. Then the method was applied to taxi ride data in Beijing, China to reveal the spatial pattern of collective human mobility.

The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road, weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road, and almost no spatio-temporal autocorrelation between the roads.

Local spatial patterns of taxi travel were also recognized. This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.

Modelling geographic accessibility to Primary Health Care Facilities: combining open data and geospatial analysis

结合开放数据和地理空间分析建立初级卫生保健设施地理可达性模型

Olanrewaju Lawal & Felix E. Anyiam

文章简介

确保所有年龄段的健康生活和促进福祉是第三个可持续发展目标（SDG）。医疗保健的不平等仍然是实现这一目标的主要挑战之一。随着城市面积的不断扩大和人口的增长，有必要定期检查各地区、州和城市地区基本便利设施的无障碍模式。

本研究采用开放数据和地理空间分析技术相结合的方法，考察了尼日利亚初级卫生保健设施的地理获取情况。由于信息差距的问题，展示一种方法可以在撒哈拉以南非洲的不同区域复制。本文使用了海拔、卫生设施、人口和网络数据的数据。

结果表明，初级卫生保健设施聚集在某些位置，如主要城市群和通往这些地方的公交线路。在首都初级卫生保健设施的密度较高。到最近初级卫生保健设施的平均行驶时间约为14分钟（标准偏差±13.30分钟），而最长约为2小时。

尼日利亚尼日尔三角洲地区的阿卡瓦伊博姆州（Akwa Ibom State）到处都是交通不便的地区。有迹象表明，大多数地方都有良好的地理位置。在我们的数据集中发现的1787个居民点中，98.3%的居民点具有良好的交通条件（<30分钟），27个居民点位于交通不便等级（31-60分钟），而两个居民点属于非常差的居民点（>60分钟）。

地理访问并不是该地区卫生保健准入的主要限制因素。因此，在计算获得医疗保健的机会时，应考虑到可及性的其他方面，以建立一个有力的衡量标准，以支持有效和高效的卫生保健规划和提供。

Ensuring healthy lives and promoting well-being for all ages is the 3rd Sustainable Development Goal (SDG). Inequality in access to health care remains one of the primary challenges in achieving the goal.

With the ever-increasing expansion of urban areas and population growth, there is a need to regularly examine the pattern of accessibility of basic amenities across regions, States and urban areas.

This study examined geographic access to Primary Health Care Facilities (PHCF) in Nigeria using the combination of open data and geospatial analysis techniques.

Thus, showcasing an approach can be replicated across different regions in Sub-Saharan Africa due to issues of information gap. Data on elevation, location of health care facilities, population and network data were utilized.

The result shows that PHCF aggregate at certain locations, e.g. major urban agglomerations, and transit route leading to these places. High concentrations are found in the capital city. The average travel time to the nearest PHCF is about 14 min (Standard Deviation ±13.30 min) while the maximum is about 2 hours.

Pockets of low accessibility areas exist across the Akwa Ibom State in the Niger Delta region of Nigeria. There is an indication that most places have good geographic access. Across the 1787 settlements identified in our dataset, 98.3% are with good access (<30 min), 27 settlements are located in the poor access class (31–60 min), while two settlements are within the very poor access class (>60 min).

Geographic access is not the main limiting factor to health care access in the region. Therefore, computation of access to health care should take into consideration other dimensions of accessibility, to create a robust measure which will support effective and efficient health care planning and delivery.

Bridging open source tools and Geoportals for interactive spatial data analytics

为交互式空间数据分析架起开源工具和地理门户网站的桥梁

Bing She, Tao Hu, Xinyan Zhu & Shuming Bao

文章简介

对于不同领域的研究人员来说，地理门户一直是空间信息的主要来源。近年来，将空间分析和地理可视化分析结合起来的趋势越来越明显。研究人员可以利用地理门户进行基本分析，而无需离线处理。在实践中，特定领域的分析通常要求研究人员集成异构数据源，利用新的统计模型，或者构建自己定制的模型。这些任务正越来越多地用Python或R等编程语言的开源工具来解决。然而，将众多的开源工具整合到地理门户平台中进行数据处理和分析是不现实的。

这项工作提供了一种探索性的努力，通过Python脚本将Geoportals和开源工具连接起来。本文所展示的地理门户是中国研究的城市和区域探索者。提供了一个python包来在本地编程环境中操作该平台。地理门户的服务器端实现了一组服务端点，允许包上载、转换和处理用户数据，并将它们无缝地集成到现有的数据集中。

本文以一个案例研究为例，说明如何使用该软件包对搜索引擎数据和基线普查数据进行综合分析。这项工作尝试了一个新的发展方向，进一步推动了地理门户向在线分析工作台的转变。

Geoportals have been the primary source of spatial information to researchers in diverse fields. Recent years have seen a growing trend to integrate spatial analysis and geovisual analytics inside Geoportals. Researchers could use the Geoportal to conduct basic analysis without offline processing. In practice, domain-specific analysis often requires researchers to integrate heterogeneous data sources, leverage new statistical models, or build their own customized models.

These tasks are increasingly being tackled with open source tools in programming languages such as Python or R. However, it is unrealistic to incorporate the numerous open source tools in a Geoportal platform for data processing and analysis. This work provides an exploratory effort to bridge Geoportals and open source tools through Python scripting.

The Geoportal demonstrated in this work is the Urban and Regional Explorer for China studies. A python package is provided to manipulate this platform in the local programming environment.

The server side of the Geoportal implements a set of service endpoints that allows the package to upload, transform, and process user data and seamlessly integrate them into the existing datasets.

A case study is provided that illustrated the use of this package to conduct integrated analyses of search engine data and baseline census data. This work attempts a new direction in Geoportal development, which could further promote the transformation of Geoportals into online analytical workbenches.

Development and implementation of a dynamic and 4D GIS based on semantic location model

基于语义定位模型的动态4D GIS的开发与实现

Xinyan Zhu, Tao Hu, Xinyue Ye, Wei Guo, Liang Huang, Hanjiang Xiong, Haojun Ai, Bing She, Qing Xiong & Lian Duan

文章简介

在大数据时代，必须提出稳健的解决方案来整合和表示不同格式、不同内容的数据，以辅助决策。目前的制图和地理信息系统解决这些问题的能力有限。

本文描述了一个自动化的综合系统，对所有潜在的相关来源进行数据融合。在该系统中，建立了一个新的语义位置模型（SemLM），用以表达语义概念和位置特征，并演示位置之间的相互关系。在SemLM中，可以分析和理解不同应用场景中不同类型的位置描述符。

此外，考虑到数据密集型计算和可视化所面临的挑战，本文以公共安全为例，实现了一个基于地点的泛信息系统（P2S）作为一个动态关联和可视化的4D系统。

In the big data era, robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making. Current cartographic and geographic information systems have limited capabilities for solving these problems.

This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources. In this system, a new Semantic Location Model (SemLM) is established to present the semantic concepts and location feature and demonstrate how locations are interrelated.

In the SemLM, various types of location descriptors in different application scenarios can be analyzed and understood. Additionally, considering the challenges involved in data-intensive computation and visualization, this paper implements a Place-based Pan-Information System (P2S) as an innovative 4D system that dynamically associates and visualizes place-based information, using public security as the case study.

Exploring the characteristics of tourism industry by analyzing consumer review contents from social media: a case study of Bamako, Mali

从社交媒体的消费者评论内容分析旅游业的特点——以Bamako, Mali为例

Sanogo Bruno, Chao Yang, Wenwen Tian, Zhong Xie & Yuanzheng Shao

文章简介

在web2.0时代，社交网络上呈现的各种各样、海量的旅游体验和评论已经成为旅游研究的重要信息。

本文运用社交媒体对Bamako旅游业进行了探索和研究。收集了来自TripAdvisor和Facebook的2000多位评论家及其对Bamako和餐厅的评论。同时，我们将官方旅游统计数据和实地调查数据整合到在线评论数据集中。运用数据挖掘和统计方法对数据进行分析，探讨Bamako旅游业的特点。

我们发现：

（i）大多数游客是出于商务目的来Bamako的，他们倾向于选择服务和安全条件更好的酒店；

（ii）社交媒体上的评论会极大地影响游客对酒店的选择；

（iii）大多数游客对Bamako的住宿服务感到满意。

In this Web 2.0 era, various and massive tourist experiences and reviews presented on social networks have become important information for tourism research.

In this paper, we apply social media to explore and study the tourism industry of Bamako, Mali. Over 2000 reviewers and their comments about Bamako’s hotels and restaurants from TripAdvisor and Facebook were collected. Also, we integrate official tourism statistic data and field surveying data into the online review dataset.

Data mining and statistic method are used to analyze the data for purpose of exploring the characteristics about tourism industry in Bamako. And we find that:

(i) Most tourists are coming to Bamako for business purpose, and they incline to choose the hotels with better service and security condition;

(ii) Comments on social media would greatly affect travelers’ choice on hotels;

(iii) Most travelers are satisfied about Bamako’s accommodation services.

关于 Geo-spatial Information Science

Geo-spatial Information Science（GSIS）是由武汉大学主办的测绘遥感专业英文期刊，主编为中国科学院院士、中国工程院院士李德仁教授。2020年9月被SCIE收录。

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投稿网址：

https://rp.tandfonline.com/submission/create?journalCode=TGSI

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