多源地理大数据支撑下的地理事件建模与分析

地理事件作为描述地理过程的基本单元,逐渐成为地理信息科学（GIS）核心研究内容。由于受人类活动数据获取限制,GIS对地理事件的建模和分析主要关注事件所引起的地理空间要素变化及要素之间的相互影响与作用机制。然而,近年来随着基于位置服务数据（LBS）爆炸式的增长和人类活动大数据定量刻画手段的快速发展,地理事件对人类活动的影响以及公众对地理事件的网络参与度都引起了多个领域的广泛关注,对地理事件的时空认知、建模方法和分析框架提出了巨大的挑战。对此,本文首先深入分析了大数据时代地理事件的概念与分类体系;其次,基于地理事件的时空语义给出了基于图模型的事件数据建模,建立了事件本体及其次生或级联事件的“节点—边”表达结构,开展了事件自身时空演化及其前“因”后“果”的形式化描述;第三,从时空数据分析与挖掘的角度,给出了大数据时代地理事件建模与分析的整体框架,拟突破传统“地理实体空间”事件探测与分析方法的局限性,融合“虚拟空间”事件发现与传播模拟思路,实现多源地理大数据支撑下的面向地理事件的人类活动多尺度时空响应与区域差异分析;最后,本文以城市暴雨事件为例诠释了本文所提出的地理事件建模与分析方法,从城市和城市内部两个尺度进行了暴雨事件与人类活动的一致性响应及区域差异分析,得到了明确的结论,验证了前文分析框架的可行性与实用性。     

山区过渡性地理空间人文自然交互性识别与分异解析

山区因其人文自然交互过程具有显著的地域性、时空分异性与不确定性,成为典型的过渡性地理空间,其类型量化识别与分异特征的解析可为山区乡村振兴背景下国土空间高质量发展提供决策依据。本文基于地理不确定性的概念构建时空变率指数,识别出中国山区过渡性地理空间分布与分区,并运用地理探测器解析其地理时空变率的驱动力谱。结果发现：中国山区过渡性地理空间总面积为238.32×10⁴ km²（约占中国陆表面积的1/4）,其地理时空变率从第一阶梯到第三阶梯呈递减趋势;全国山区过渡性地理空间可分为12个分区,其中昆仑—祁连山分区面积最大;人文驱动因子对人口与土地利用的时空变率解析力最强,基础地形因子对植被覆盖时空变率与地理时空变率指数的解析力最为显著,各因子中海拔与夜间灯光的解析驱动力最强。整体而言,人文要素的时空动态均对东南山区过渡性地理空间具有显著的塑造性,而西北山区过渡性地理空间主要受到自然要素的时间变化和人文要素的空间变化所驱动。本文为山区过渡性地理空间的不确定性、多样性与人地关系地域性的定量研究提供了新的见解与启示。     

From Geographic Holism to Geographic Information System

Substantial changes in a core idea of geography, integration, have occurred since Alexander von Humboldt published Kosmos (1845-1862). These changes are part of a larger shift in Western civilization to mechanistic reasoning. This shift led to the strengthening of system-based analysis, central to the development of geographic information systems (GIS). The duality of holism and the systems approach has led to an apparent contradiction in geography. R. Hartshorne in The Nature of Geography described this contradiction, but as did Alfred Hettner and Emil Wisotzki before, moved to partial systems as the core concept of geographic integration. Hartshorne's concept of vertical integration is the antecedent for the ubiquitous GIS layer model. The reduction of geographic relationships and processes to mechanistic components (layers) aids the systematic approach, but may lessen geographic understanding of a place's interrelationships. Although the partiality of the system approach was already acknowledged by Finch and Hartshorne in the 1930s, the tension between holistic and system approaches in geography remains. Holism and system-based approaches are indeed complementary methods for developing geographic understanding. Using holistic approaches to understand geographic phenomena, before we teleologically (following a purpose) analyze phenomena as a system, extends GIS to include broader interrelationships of geography in specific locations.     

Design and Usability of an Enhanced Geographic Information System for Exploration of Multivariate Health Statistics

Abstract

The multidimensional nature of many types of data in modern geography calls for creative and innovative approaches to their analysis. Statisticians have recently developed methods for exploring and visualizing large, multivariate datasets, but cartographers and geographers in general have only recently begun to integrate these methods for use with spatial and spatiotemporal datasets that are multivariate in character. This article will present an example of such an integration—an environment for visualization of health statistics—as a case study to demonstrate the philosophical and practical advantages of geovisualization systems for the exploration of complex spatiotemporal information. Emphasis is placed on the encouragement of creative thinking about geographic phenomena through the use of such data-rich graphical tools.

     

Spatiotemporal characteristics of urban air quality in China and geographic detection of their determinants

Ambient air pollution brought by the rapid economic development and industrial production in China has exerted a significant influence on socio-economic activities and public health, especially in the densely populated urban areas. Therefore, scientific examination of regional variation of urban air quality and its dominant factors is of great importance to regional environmental management. Based on daily air quality index (AQI) datasets spanning from 2014 to 2016, this study analysed the spatiotemporal characteristics of air quality across different regions throughout China and ascertained the determinants of urban air quality in disparate regions. The main findings are as follows: (1) The annual average value of the urban AQI in China decreased from 2014 to 2016, indicating a desirable trend in air quality at the national scale. (2) The attainment rate of the urban AQI exhibited an apparent spatially stratified heterogeneity, wherein North China retained a high AQI value. The increase of Moran’s I Index reported an apparent spillover effect among adjacent regions. (3) Both at the national and regional scales, the seasonal tendency of air quality in each year is similar, wherein good in summer and relatively poor in winter. (4) Results drawn from the Geographic Detector analysis show that dominant factors influencing AQI vary significantly across urban agglomerations. Topographical and meteorological variations in urban areas may lead to complex spatiotemporal variations in pollutant concentration. Whereas given the same natural conditions, the human-dominated factors, such as industrial structure and urban form, exert significant impacts on urban air quality.The spatial spillover effects and regional heterogeneity of urban air quality illustrated in this study suggest the governments and institutions should set priority to the importance of regional cooperation and collaboration in light of environment regulation and pollution prevention.     

Coupling maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists

Modeling the geographic distribution of tourists at a tourist destination is crucial when it comes to enhancing the destination’s resilience to disasters and crises, as it enables the efficient allocation of limited resources to precise geographic locations. Seldom have existing studies explored the geographic distribution of tourists through understanding the mechanisms behind it. This article proposes to couple maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists in order to facilitate disaster and crisis management at tourist destinations. As one of the most popular tourist destinations in the United States, San Diego was chosen as the study area to demonstrate the proposed approach. We modeled the tourist geographic distribution in the study area by quantifying the relationship between the distribution and five environmental factors, including land use, land parcel, elevation, distance to the nearest major road and distance to the nearest transit stop. The geographic distribution’s dependency on and sensitivity to the environmental factors were uncovered. The model was subsequently applied to estimate the potential impacts of one simulated tsunami disaster and one simulated traffic breakdown due to crisis events such as a political protest or a fire hazard. As such, the effectiveness of the approach has been demonstrated with specific disaster and crisis scenarios.     

A Pedagogic Framework to Link GIS to the Intellectual Core of Geography

Abstract

This paper aims to develop a new pedagogic framework for teaching GIS at the college and university level using Berry's geographic matrix. By synthesizing different schools of thought, this paper argues that GIS education essentially involves two aspects—how to teach about GIS and how to teach with GIS. Berry's geographic matrix can be used to tie these dual aspects of GIS education together neatly. As an abstract representation of geographical phenomena, the geographic matrix embeds all three entities of GIS—location, attribute, time—and thus can help GIS instructors teach about GIS. As a synthesis of geographical approaches, the geographic matrix can assist GIS instructors teach with GIS. This paper demonstrates that GIS is actually an implementation of Berry's geographic matrix. Furthermore, the 10 approaches to geographical analysis, originally proposed by Berry for the geographic matrix, can be executed routinely in a GIS environment. By incorporating Berry's geographic matrix into GIS education, teachers can enable students to surpass technical issues and to appreciate the conceptual and functional linkages between GIS and geography's intellectual core.     

地理科学发展与新技术应用

当代计算机、互联网、航天航空、自动化和传感网、环境和生态修复等技术发展很快,并渗透到许多基础与应用基础研究学科。以综合性、交叉性和区域性为特点的地理学借力于新技术应用,学科发展得到有力促进,突出表现包括：① 研究时空拓展到近实时和全球,基本解决异域和极端地理环境数据难获取问题;② 数据获取方式和渠道多样性促进了数据的爆发性增加,对规律和格局的分析从依赖有限时空表观信息发展到依靠新技术获取高时空动态数据开展大数据挖掘;③ 研究内容从静态知识获取、机理分析拓展到包括生态修复和环境治理等能动性的工作;④ 学科发展呈现领域拓展和新技术学科交叉趋势,地理学通过与新技术进一步融合发展获得新生命力。借助新技术和地理大数据“燃料”的注入,新时期地理科学发展将在全球和区域社会经济建设过程中通过提供“复方”解决方案而发挥重要的作用。     

From Geographic Holism to Geographic Information System

Substantial changes in a core idea of geography, integration, have occurred since Alexander von Humboldt published Kosmos (1845-1862). These changes are part of a larger shift in Western civilization to mechanistic reasoning. This shift led to the strengthening of system-based analysis, central to the development of geographic information systems (GIS). The duality of holism and the systems approach has led to an apparent contradiction in geography. R. Hartshorne in The Nature of Geography described this contradiction, but as did Alfred Hettner and Emil Wisotzki before, moved to partial systems as the core concept of geographic integration. Hartshorne's concept of vertical integration is the antecedent for the ubiquitous GIS layer model. The reduction of geographic relationships and processes to mechanistic components (layers) aids the systematic approach, but may lessen geographic understanding of a place's interrelationships. Although the partiality of the system approach was already acknowledged by Finch and Hartshorne in the 1930s, the tension between holistic and system approaches in geography remains. Holism and system-based approaches are indeed complementary methods for developing geographic understanding. Using holistic approaches to understand geographic phenomena, before we teleologically (following a purpose) analyze phenomena as a system, extends GIS to include broader interrelationships of geography in specific locations.     

Spatial Correlation between Traffic and Air Pollution in Beijing

Many studies have attempted to model the sophisticated influence of traffic emissions on air pollution, but most models only calculate the contribution of traffic emissions near monitoring sites. It is difficult to observe the near-surface dynamics such as wind, rain, and human activities and precisely distinguish traffic emissions. These obstacles make model simulation very expensive in practice. The regional distribution patterns that can help adjust policies and actions taken remain unknown. Therefore, this article proposes a grid-oriented geostatistics-based approach to overcome these obstacles. We chose central Beijing as the study area. An experiment implemented the approach on data collected from Global Positioning System navigation software, car rental companies, and meteorology stations. The results suggest that the northwest area of Beijing has high traffic-related air pollution (TRAP) and the southeast area has low TRAP. Unlike modeling-based methods, this work uses geostatistical methods to directly study the spatiotemporal connections between traffic and PM_2.5 (particulate matter with diameter less than 2.5?μm) from the phenomenon. The calculation is conducted under no hypotheses and has little risk of producing results contradictory to facts. This work provides a reference for future TRAP research on directly learning from the phenomenon and assists decision makers with seamless spatiotemporal heat maps of TRAP distribution. Key Words: Beijing, geospatial statistics, PM_2.5, spatial correlation, traffic-related air pollution.     

A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena

Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy.     

虚拟地理环境中尺度适宜性问题的探讨

本文沿着地理学语言的演化过程,讨论了地理学中尺度概念的演变;针对虚拟地理环境的框架结构,依据尺度概念的维度、类别和组成因素分析了四组尺度适宜性问题,并讨论了在虚拟地理环境搭建和应用过程中各种尺度适宜性之间的关系。以香港区域气象过程模拟为例,应用多尺度地形数据和模型,简要分析了在空间维上,测量尺度类别中组成因素（分辨率） 层次的尺度适宜性。该案例不只表明尺度适宜性对动态地理过程问题求解的重要影响,同时表明对尺度适宜性的讨论有助于其认知与分解,丰富虚拟地理环境的理论与方法。     

Effect of climate and seasonality on depressed mood among twitter users

Location-based social media provide an enormous stream of data about humans' life and behavior. With geospatial methods, those data can offer rich insights into public health. In this research, we study the effect of climate and seasonality on the prevalence of depression in Twitter users in the U.S. Text mining and geospatial methods are used to detect tweets related to depression and their spatiotemporal patterns at the scale of Metropolitan Statistical Area. We find the relationship between depression rates, climate risk factors and seasonality are varied and geographically localized. The same climate measure may have opposite association with depression rates at different places. Relative humidity, temperature, sea level pressure, precipitation, snowfall, weed speed, globe solar radiation, and length of day all contribute to the geographic variations of depression rates. A conceptual compact map is designed to visualize scattered geographic phenomena in a large area. We also propose a three-stage framework that semi-automatically detects and analyzes geographically distributed health issues using location-based social media data.     

A method for discovery and analysis of temporal patterns in complex event data

Pattern analysis techniques currently common within geography tend to focus either on characterizing patterns of spatial and/or temporal recurrence of a single event type (e.g., incidence of flu cases) or on comparing sequences of a limited number of event types where relationships between events are already represented in the data (e.g., movement patterns). The availability of large amounts of multivariate spatiotemporal data, however, requires new methods for pattern analysis. Here, we present a technique for finding associations among many different event types where the associations among these varying event types are not explicitly represented in the data or known in advance. This pattern discovery method, known as T-pattern analysis, was first developed within the field of psychology for the purpose of finding patterns in personal interactions. We have adapted and extended the T-pattern method to take the unique characteristics of geographic data into account and implemented it within a geovisualization toolkit for an integrated computational-geovisual environment we call STempo. To demonstrate how T-pattern analysis can be employed in geographic research for discovering patterns in complex spatiotemporal data, we describe a case study featuring events from news reports about Yemen during the Arab Spring of 2011–2012. Using supplementary data from the Global Database of Events, Language, and Tone, we briefly summarize and reference a separate validation study, then evaluate the scalability of the T-pattern approach. We conclude with ideas for further extensions of the T-pattern technique to increase its utility for spatiotemporal analysis.     

Teaching Geography's four traditions with Poetry

Abstract

Poetry is a powerful form of writing that has received relatively little attention from geography educators. However, most poetry is imbued with explicit and vivid references to physical and human phenomena over space, and is thus a source of information that may help illustrate a variety of geographic concepts. This article uses William Pattison's four traditions of geography as a framework for illustrating the explicitly spatial concepts present in selected poetry. Through the poetry of authors like Walt Whitman, Robert Frost, and Maya Angelou, this work introduces a new perspective from which to teach and to learn familiar geographic themes.     

A bidirectional path-finding algorithm and data structure for maritime routing

Route planning is an important problem for many real-time applications in open and complex environments. The maritime domain is a relevant example of such environments where dynamic phenomena and navigation constraints generate difficult route finding problems. This paper develops a spatial data structure that supports the search for an optimal route between two locations while minimizing a cost function. Although various search algorithms have been proposed so far (e.g. breadth-first search, bidirectional breadth-first search, Dijkstra’s algorithm, A^*, etc.), this approach provides a bidirectional dynamic routing algorithm which is based on hexagonal meshes and an iterative deepening A^* (IDA^*) algorithm, and a front to front strategy using a dynamic graph that facilitates data accessibility. The whole approach is applied to the context of maritime navigation, taking into account navigation hazards and restricted areas. The algorithm developed searches for optimal routes while minimizing distance and computational time.     

A framework to enhance semantic flexibility for analysis of distributed phenomena

While some geographic phenomena hold uniform properties, such as land‐use zones, many geographic phenomena are distributed such that their properties vary across an extended area. While such distributed phenomena are best represented as continuous surfaces, individual objects (or features) often emerge among clusters of high or low values in a field. For example, areas of relatively high elevation may be viewed as hills, while flat low‐lying areas are perceived as plains in a terrain. A comprehensive spatial analysis of distributed phenomena should examine both the spatial variance of its attribute surfaces and the characteristics of individual objects embedded in the field. An immediate research challenge to meet such spatial analysis needs is that these emerging features often have vague boundaries that vary according to the use and the user. The nature, and even existence, of these objects depend upon the range of values, or thresholds, used to define them. We propose a representation framework that takes a dual raster‐vector approach to capture both field‐ and object‐like characteristics of distributed phenomena and maintain multiple representations of embedded features delineated by boundaries that are likely to be relevant for the expected uses of the data. We demonstrate how boundaries influence the analysis and understanding of spatiotemporal characteristics of distributed phenomena. Using precipitation as a proof of concept, we show how the proposed framework enhances semantic flexibility in spatiotemporal query and analysis of distributed phenomena in geographic information systems.     

An integrated web-based air pollution decision support system – a prototype

To efficiently and effectively monitor and mitigate air pollution in the urban environment, it is of paramount importance to integrate into a unified whole air pollutant concentration databases coming from different sources including the ground-based stations, mobile sensors, remote sensing, atmospheric-chemical-transport models and social media for the analysis and unraveling of the complex air pollution processes in space and time. This study constructs and implements for the first time a prototype of the fully integrated air pollution decision support system (APDSS) that put together in an integrated manner all relevant multi-scale, multi-type and multi-source data for decision-making on urban air pollution. The prototype contains the main system that handles the multi-source, multi-type and multi-scale databases, queries, visualization and data mining algorithms and the integrated modules that individually and holistically capitalize on the power of the ground-based stations, ground and aerial mobile sensors, satellite-borne remote-sensing technologies, atmospheric-chemical-transport models and social media. It renders a solid scientific foundation and system development methodology for the study of the spatiotemporal air pollution profiles crucial to the mitigation of urban air pollution. Real-life applications of the prototype are employed to illustrate the functionality of the APDSS.     

地理大数据为地理复杂性研究提供新机遇

大数据之风自2010年席卷全球,已在科学、工程和社会等领域产生深远影响。本文首先从地理大数据、第四范式以及非线性复杂地理系统3组基本概念出发,剖析上述3组概念之间的科学联系与相互支撑作用,提出大数据和第四范式为地理复杂性研究提供新机遇。其后,探讨如何利用大数据和复杂性科学的理论方法开展地理复杂性研究。基于地理大数据,可以通过统计物理学的系列指标描述现实地理世界的复杂非线性特征,同时,还可利用深度学习、复杂网络、多智能体等方法,实现复杂非线性地理系统的推演和模拟。上述方法对认知地理现象和过程的复杂性,对复杂地理系统的分析、模拟、反演与预测有重要作用。最后,提出地理大数据和复杂性科学相互支撑可能成为21世纪地理学的主流科学方法。