Home range and habitat: Using platial characteristics to define urban areas from the bottom up

The spatial representation of a city is typically formed by top‐down jurisdictional boundaries. A parallel approach would be to consider representing a city based on platial characteristics, that is, a bottom‐up landscape created through individual and collectively derived representations. This study contributes to this discourse through the exploratory examination of the ecology notions of home range and habitat applied to humans in an urban context. Using spatial data collected through a WebGIS platform, we employ a spatial definition of sense of place and social capital to understand the platial nature of the city and, simultaneously, defining home range and habitat as platial notions. We found spatial variability among individual home range and habitat and the difficulty of traditional administrative boundaries to represent these areas. This research defines and presents home range and habitat to partially describe the emergent nature of platial theory and explores their operationalization at the urban level.     

Location Based Social Networks – Definition,Current State of the Art and Research Agenda

The growing penetration of GPS equipped smartphones allowing users to constantly share geographic information on their current whereabouts has led the way to an augmentation of existing Social Network Sites with location‐based features or the creation of new ones exclusively around geographic information. Within these Location Based Social Networks vast amounts of geographic information are allocated, which attracted the attention of researchers with various scientific backgrounds. This article presents a comprehensive definition of this special type of Social Network Site and an overview of research activities, which are currently conducted using the data. Finally, a research agenda for GIScience is drawn from existing research directions.     

The Landscape of GIScience Publications 1997–2007: An Empirical Investigation with Latent Semantic Analysis

This article reports on an empirical study of the trends and patterns of research activities in Geographic Information Science (GIScience) during the years 1997–2007. The GIScience research priorities identified by the University Consortium of Geographic Information Science (UCGIS) were used as guidelines to examine the 985 research articles published in six well‐recognized academic journals. Latent Semantic Analysis (LSA) was employed to investigate the association among the different GIScience research themes. The spatial and temporal patterns of the association between the publications and the different GIScience themes were examined to show the development of GIScience research during the study period. Furthermore, correlation analyses between the publications were conducted following the LSA results to reveal GIScience research networks, including the networks of the published articles and those formed by the research places. In this article, we applied an approach that was developed within information science to depict what GIS research activities were conducted when and where and how they connect to each other through sharing common research themes. The related findings pave the way for future efforts to describe the paradigm of GIScience as well as the pattern of GIScience research.     

Enriching the GIScience research agenda: Fusing augmented reality and location‐based social networks

Augmented reality (AR) overlays real‐world views or scenes with virtual, computer‐generated objects that appear to visually coexist in the same space. Location‐based social networks (LBSNs) are platforms for individuals to be connected through the interdependency derived from their physical locations and their location‐tagged social media content. Current research and development in both areas focuses on integrating mobile‐based AR and LBSNs. Several applications (e.g., Sekai Camera and Wallame) have been developed and commercialized successfully. However, little research has been done on the potential impacts and successful evaluation methods of AR‐integrated LBSNs in the GIScience field. To close this gap, the article outlines the impacts and benefits of AR‐integrated LBSNs and highlights the importance of LBSNs in GIScience research. Based on the status quo of AR‐integrated LBSNs, this article discusses—from theoretical and application‐oriented perspectives—how AR‐integrated LBSNs could enrich the GIScience research agenda in three aspects: data conflation, platial GIS, and multimedia storytelling. The article concludes with guidelines on visualization, functionality, and ethics that aim to help users develop and evaluate AR‐integrated LBSNs.     

Los Angeles as a digital place: The geographies of user‐generated content

Online representations of places are becoming pivotal in informing our understanding of urban life. Content production on online platforms is grounded in the geography of their users and their digital infrastructure. These constraints shape place representation, that is, the amount, quality, and type of digital information available in a geographic area. In this article we study the place representation of user‐generated content (UGC) in Los Angeles County, relating the spatial distribution of the data to its geo‐demographic context. Adopting a comparative and multi‐platform approach, this quantitative analysis investigates the spatial relationship between four diverse UGC datasets and their context at the census tract level (about 685,000 geo‐located tweets, 9,700 Wikipedia pages, 4 million OpenStreetMap objects, and 180,000 Foursquare venues). The context includes the ethnicity, age, income, education, and deprivation of residents, as well as public infrastructure. An exploratory spatial analysis and regression‐based models indicate that the four UGC platforms possess distinct geographies of place representation. To a moderate extent, the presence of Twitter, OpenStreetMap, and Foursquare data is influenced by population density, ethnicity, education, and income. However, each platform responds to different socio‐economic factors and clusters emerge in disparate hotspots. Unexpectedly, Twitter data tend to be located in denser, more deprived areas, and the geography of Wikipedia appears peculiar and harder to explain. These trends are compared with previous findings for the area of Greater London.     

VGDI – Advancing the Concept: Volunteered Geo‐Dynamic Information and its Benefits for Population Dynamics Modeling

The concept of Volunteered Geographic Information (VGI) has progressed from being an exotic prospect to making a profound impact on GIScience and geography in general, as initially anticipated. However, while massive and manifold data is continuously produced voluntarily and applications are built for information and knowledge extraction, the initially introduced concept of VGI lacks certain methodological perspectives in this regard which have not been fully elaborated. In this article we highlight and discuss an important gap in this concept, i.e. the lack of formal acknowledgment of temporal aspects. By coining the proposed advanced framework ‘Volunteered Geo‐Dynamic Information’ (VGDI), we attempt to lay the ground for full conceptual and applied spatio‐temporal integration. To illustrate that integrative approach of VGDI and its benefits, we describe the potential impact on the field of dynamic population distribution modeling. While traditional approaches in that domain rely on survey‐based data and statistics as well as static geographic information, the use of VGDI enables a dynamic setup. Foursquare venue and user check‐in data are presented for a test site in Lisbon, Portugal. Two core modules of spatio‐temporal population assessment are thereby addressed, namely time use profiling and target zone characterization, motivated by the potential integration in existing population dynamics frameworks such as the DynaPop model.     

Geocomputation over the Emerging Heterogeneous Computing Infrastructure

Emerging computer architectures and systems that combine multi‐core CPUs and accelerator technologies, like many‐core Graphic Processing Units (GPUs) and Intel's Many Integrated Core (MIC) coprocessors, would provide substantial computing power for many time‐consuming spatial‐temporal computation and applications. Although a distributed computing environment is suitable for large‐scale geospatial computation, emerging advanced computing infrastructure remains unexplored in GIScience applications. This article introduces three categories of geospatial applications by effectively exploiting clusters of CPUs, GPUs and MICs for comparative analysis. Within these three benchmark tests, the GPU clusters exemplify advantages in the use case of embarrassingly parallelism. For spatial computation that has light communication between the computing nodes, GPU clusters present a similar performance to that of the MIC clusters when large data is applied. For applications that have intensive data communication between the computing nodes, MIC clusters could display better performance than GPU clusters. This conclusion will be beneficial to the future endeavors of the GIScience community to deploy the emerging heterogeneous computing infrastructure efficiently to achieve high or better performance spatial computation over big data.     

Mobile Mapping and Geographic Information Systems

Mobile mapping and geographic information systems may represent a new paradigm for cartography and GIScience. This short foreword to the special issue introduces the three papers that follow, briefly surveys the growing literature of field and mobile GIS, and discusses the emerging literature surrounding wearable GIS and their augmented reality display systems. The UCGIS research agenda for mobile and distributed computing is presented, as are the calls for additional research in the papers of the special issue. Calls for a comprehensive review article of the field from a cartography/GIScience perspective, and for involvement in the research agenda of mobile systems, are made.     

Critical Issues in Participatory GIS: Deconstructions, Reconstructions, and New Research Directions

In the mid‐1990s, several critical texts raised concerns about the social, political, and epistemological implications of GIS. Subsequent responses to these critiques have fundamentally altered the technological, political, and intellectual practices of GIScience. Participatory GIS, for instance, has intervened in multiple ways to try to ameliorate uneven access to GIS and digital spatial data and diversify the forms of spatial knowledge and spatial logic that may be incorporated in a GIS. While directly addressing core elements of the ‘GIS & Society’ critique, these reconstructions of a critical GIScience introduce their own ambiguities with respect to access, equity, digital representation of spatial knowledge, and epistemologies of new GIS research practices. In this paper, I examine some of the new and persistent ambiguities of participatory GIS that bear inclusion in future critical GIScience research.     

Description and characterization of place properties using topic modeling on georeferenced tags

ABSTRACT

User-Generated Content (UGC) provides a potential data source which can help us to better describe and understand how places are conceptualized, and in turn better represent the places in Geographic Information Science (GIScience). In this article, we aim at aggregating the shared meanings associated with places and linking these to a conceptual model of place. Our focus is on the metadata of Flickr images, in the form of locations and tags. We use topic modeling to identify regions associated with shared meanings. We choose a grid approach and generate topics associated with one or more cells using Latent Dirichlet Allocation. We analyze the sensitivity of our results to both grid resolution and the chosen number of topics using a range of measures including corpus distance and the coherence value. Using a resolution of 500 m and with 40 topics, we are able to generate meaningful topics which characterize places in London based on 954 unique tags associated with around 300,000 images and more than 7000 individuals.     

Geographic information science as a multidisciplinary and multiparadigmatic field

Despite the fact that the field of GIScience has been around for two decades, there is still little agreement as to the exact contents and boundaries of the field. Many authors have dedicated several publications concerned with defining the field, yet little consensus has been reached. Because of the highly dynamic nature of the discipline, new areas are constantly added and rigid borders may have a constraining influence on the field. On the contrary, one agreed part of the many GIScience definitions is the search for general principles, but principles for what? Can we define one field or discipline or all potential fields using geospatial information? Probably not. Therefore, rather than attempting to demarcate exact boundaries for GIScience as a discipline or a multidisciplinary field in order to prove its respectability, we herein attempt to analyze the contents of such a dynamic field on the basis of scientific literature and to assess the multidisciplinary and multiparadigmatic nature of GIScience. Such a discussion is not purely of academic nature, but also bears implications beyond academic discourse, in terms of external scientific funding and research grants. We question whether there is a “dominant” paradigmatic approach in GIScience and identify a need for adopting a multiparadigmatic view to accommodate the multifaceted nature of space, spatial representations, and the societal implications of geospatial information.     

Approaches for Linking People,Place, and Environment for Human Dimensions Research

Abstract

Much of the human dimensions of environmental change research emphasize the mapping and modeling of land use and land cover patterns over space and time, and the linkages between people, place, and environment as proximate and distal forces of landscape dynamics. Spatial digital technologies, framed within a GIScience (GISc) context, figure prominently in the characterization of land use and land cover through remote sensing technologies, and in the assessment of social and demographic factors and local and regional site and situation considerations achieved through global positioning systems, data visualizations, and spatial and statistical analyses. Here, we describe some fundamental approaches for linking data across thematic domains, essential for the study of human‐environment interactions. The goal is to generate compatible data sets that extend across social, biophysical, and geographical domains so that the causes and consequences of land use and land cover dynamics might be explored within a spatially‐explicit context.     

空间尺度基础性问题研究

论述了地理信息科学领域中空间尺度的研究意义,介绍了空间尺度的具体内涵和定义,分析了空间尺度各内涵之间的内在联系和制约关系。     

新媒体语境下地理信息科学专业学生的空间思维培养

空间思维是地理科学认知学习与研究的基本思维模式。为满足新媒体融合背景下地理信息科学专业的素质人才培养需求,本文设计了包括地理空间模拟体验、空间信息地图绘读、隐喻信息语义认知和地理系统综合分析等针对空间思维能力训练的教学内容;明晰了地理空间思维在信息感知与整理、隐含信息挖掘、信息表达视觉化、行业服务支撑、空间治理决策等新媒体信息深度挖掘方向上的应用。本文研究有利于本科学生充分利用新媒体学习工具、系统化专业知识体系和提高自身地理空间思维素养,可以为地理信息科学本科专业教学提供有益的探索。     

Geons – domain-specific regionalization of space

The design of methods and tools to build adequate representations of complex geographical phenomena in a way that spatial patterns are emphasized is one of the core objectives of GIScience. In this paper, we build on the concept of geons as a strategy to represent and analyze latent spatial phenomena across different geographical scales (local, national, regional) incorporating domain-specific expert knowledge. Focusing on two types, we illustrate and exemplify how geons are generated and explored. So-called composite geons represent functional land-use classes, required for regional planning purposes. They are created via class modeling to translate interpretation schemes from mapping keys. Integrated geons, on the other hand, address abstract, yet policy-relevant phenomena such as societal vulnerability to hazards. They are delineated by regionalizing continuous geospatial data sets representing relevant indicators in a multidimensional variable space. Using the geon approach, we create spatially exhaustive sets of units, scalable to the level of policy intervention, homogenous in their domain-specific response, and independent from any predefined boundaries. From a GIScience perspective, we discuss either type of geons in a semantic hierarchy of geographic information constructs. Despite its validity for decision-making and its transferability across scales and application fields, the delineation of geons requires further methodological research to assess their statistical and conceptual robustness.     

空间信息的语言学概念模型

文章以空间信息与语言同为人类进行空间相关交流手段这一功能隐喻为基础,总结地图学、地理信息科学、知识工程等学科与语言学模型相关的理论研究成果,探讨空间信息语言学模型对地理信息科学发展的理论意义,包括学科范式观点、本体论观点、方法论观点和定性观点等方面的意义。     

Opensource spatial optimization in GIScience for strategic positioning

Spatial optimization involving multiple objectives is commonly needed to address complex planning and management issues yet associated models and problems are exceptionally difficult and complicated to solve. Open-source approaches have received attention because they offer potential for making spatial optimization more broadly available, and other GIScience methods, to support analysis and decision-making. Beyond this, the ability to integrate GIScience functions through open-source alternatives offers great potential to decrease reliance on commercial, proprietary software as well as enable broader use and adoption of, and reliance on, spatial optimization approaches. This article details an important bi-objective spatial problem along with open-source capabilities for solution as an example of challenges faced and opportunities possible. A number of solution approaches are considered, providing an opportunity to assess computational requirements, functionality, solution quality and issues encountered. Empirical applications associated with emergency response, healthcare access, food processing and strategic player positioning are detailed. This study highlights capabilities, limitations and challenges for open-source science in addressing a class of multi-objective spatial optimization problems.     

Place and city: Operationalizing sense of place and social capital in the urban context

The academic interest in social concepts in city contexts, such as sense of place and social capital, has been growing in the last decades. We present a systematic literature review that confirms the strong relationship between sense of place and social capital, from a social sciences point‐of‐view. It also reveal that little attention has been paid to their spatial dimensions at the urban level, thereby missing the chance to exploit socio‐spatial knowledge to improve the day‐to‐day life in and functioning of the city (e.g. in planning processes, citizen participation, civic engagement). We therefore examine sense of place and social capital from a Geographic Information Science (GISc) viewpoint, and present a formal conceptualization and initial theoretical framework which explicitly describes both concepts, and the relation between them, within the context of a city and from a spatial point of view.     

A name‐led approach to profile urban places based on geotagged Twitter data

Place is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowdsourced social media data present a valuable opportunity to develop bottom‐up inferences of places that are integral to social activities and settings. Conventional location‐led approaches use a predefined spatial unit to associate data and space with places, which cannot capture the richness of urban places (i.e., spatial extents and their dynamic functions). This article develops a name‐led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden Borough, London and the results are evaluated through comparisons to the Foursquare point of interest data. This name‐led approach enables the shift from space‐based analysis to place‐based analysis of urban space.