Basic-Level Geographic Categories*

As we experience places, we learn about those places and generalize information into more abstract geographic categories. Rosch's basic-level theory argues that information known about objects is stored in our memories in a three-layered hierarchy. Data that could be used to test this theory in a geographic context was generated by having subjects make lists of activities, characteristics, and parts associated with 11 familiar geographic categories. An analysis of the distribution of information among the geographic categories confirmed two basic predictions of Rosch's theory. Significantly more information was stored in the basic-level geographic categories country, region, state, city, and neighborhood than in the superordinate category place. Significantly more information was not stored in subordinate categories home country, home region, home state, home city, and home neighborhood. The results suggest that geographic information is efficiently stored in memory so that much of what we know about geographic space is stored in basic-level categories that are both distinctive and informative.     

Enriching user profiles using geo-social place semantics in geo-folksonomies

Geo-folksonomies link social web users to geographic places through the tags users choose to label the places with. These tags can be a valuable source of information about the user’s perception of place and can reflect their experiences and activities in the places they label. By analysing the associations between users, places and tags, an understanding of a place and its relationships with other places can be drawn. This place characterisation is unique, dynamic and reflects the perception of a particular user community that generated the geo-folksonomy. In this work, an approach is proposed to analysing geo-folksonomies that builds on and extends existing statistical methods by considering specific concepts of relevance to geographic place resources, namely, place types and place-related activities, and by building a place ontology to encode those concepts and relationships. The folksonomy analysis and evaluation are demonstrated using a realistic geo-folksonomy data set. The resulting ontology is used to build user profiles from the folksonomy. The derived profiles reflect the association between users and the specific places they tag as well as other places with relevant associated place type and activities. The methods proposed here provide the potential for many interesting and useful applications, including the harvesting of useful insight on geographic space and employing the derived user profiles to enhance the search experience and to identify similarities between users based on their association to geographic places.     

地理信息科学发展与技术应用

本文回顾了中国科学院地理科学与资源研究所在地理信息科学研究与技术应用方面的历史过程,从早期的测量和制图的研究,到开创中国地理信息学科,建立资源与环境信息系统国家重点实验室的历史,是中国特色原创地理信息理论发展的历史,是中国具备自主研发世界级地理信息软件的历史,是地理信息为国家重大战略提供坚实科技支撑的历史。本文主要从地图学、地学遥感、地理信息科学、地学数据共享、重大技术突破和国家战略支撑等方面进行概述,最后从地学知识图谱、地理大数据分析、遥感人工智能、地理系统模拟和知识服务角度展望地理科学发展的新科学范式。     

Making Space for the Citizen in Geographic Education

Abstract

This article argues that we need to reconsider the connection between citizenship and geography. Although geographic education purports to prepare citizens, the possibilities for this relationship have been addressed by few scholars. The study shows that citizenship is situated in places and students’ actions as citizens reinscribe the meaning of these places. Because of this intersection, educators must be attentive to how they teach about places, particularly the ongoing interconnection between place and identity. One of the identities common in the teaching of places is citizenship. Geography instructors need to be thoughtful about their role as citizenship educators.     

Mining query-driven contexts for geographic and temporal search

The explosive growth of geographic and temporal data has attracted much attention in information retrieval (IR) field. Since geographic and temporal information are often available in unstructured text, the IR task becomes a non-straightforward process. In this article, we propose a novel geo-temporal context mining approach and a geo-temporal ranking model for improving the search performance. Queries target implicitly ‘what’, ‘when’ and ‘where’ components. We model geographic and temporal query-dependent frequent patterns, called contexts. These contexts are derived based on extracting and ranking geographic and temporal entities found in pseudo-relevance feedback documents. Two methods are proposed for inferring the query-dependent contexts: (1) a frequency-based statistical approach and (2) a frequent pattern mining approach using a support threshold. The derived geographic and temporal query contexts are then exploited into a probabilistic ranking model. Finally, geographic, temporal and content-based scores are combined together for improving the geo-temporal search performance. We evaluate our approach on the New York Times news collection. The experimental results show that our proposed approach outperforms significantly a well-known baseline search, namely the probabilistic BM25 ranking model and state-of-the-art approaches in the field as well.     

Modelling the history of semi-structured geographical entities

Geographical entities are characterized by rather complex structures. They involve space and thematic information, which is subject to change in time, while history should be maintained. On the other hand, these structures may be irregular (i.e. they do not necessarily conform to a fixed schema), because associated data is usually collected based on different specifications and multiple resolutions. Hence, the representation of geographical entities in traditional data models, such as the relational or object-oriented, is not always feasible. In this respect, this paper investigates the use of semi-structured data (SSD) models—an innovative approach recently developed in Information Technology—for modelling dynamic geographical entities. A framework for the representation of geographic entities in Object Exchange Model (OEM), a popular model for semi-structured data, is introduced. Additionally, it is shown how useful information can be extracted from such a representation using the LOREL query language for SSD. A simplified case study in the application domain of cadastre involving SSD is examined closely.     

Location approximation for local search services using natural language hints

Local search services allow a user to search for businesses that satisfy a given geographical constraint. In contrast to traditional web search engines, current local search services rely heavily on static, structured data. Although this yields very accurate systems, it also implies a limited coverage, and limited support for using landmarks and neighborhood names in queries. To overcome these limitations, we propose to augment the structured information available to a local search service, based on the vast amount of unstructured and semi‐structured data available on the web. This requires a computational framework to represent vague natural language information about the nearness of places, as well as the spatial extent of vague neighborhoods. In this paper, we propose such a framework based on fuzzy set theory, and show how natural language information can be translated into this framework. We provide experimental results that show the effectiveness of the proposed techniques, and demonstrate that local search based on natural language hints about the location of places with an unknown address, is feasible.     

基于SoftGIS的土地利用地域性认同测度研究——以荷兰为例

在剖析地域性认同双重内涵及其与土地利用关系的基础上,结合SoftGIS技术,研究构建土地利用地域性认同测度方法,并以荷兰为例进行实证分析。数据分析显示,传统GIS数据可与土地利用认知数据相融合,基于核密度修正法构建的土地利用地域性认同指数可测度地域性认同的空间差异,应用于土地利用评价或规划决策。结论表明,测度平台在问卷设计和调查空间尺度上具有灵活性,数据收集快捷,通过平台的高清卫星地图定位,调查对象可反馈空间准确、情景真实的土地利用地域性认同信息。     

Books,Places, Regions—A Natural Blend

Abstract

Children love books, and through carefully chosen ones geographic concepts can be conveyed. Books are good resources—great background material for describing a place, emphasizing the culture of a region, highlighting a geographical issue, or clarifying where a place is in the world. This article explains how literature encourages geographic thinking, builds geographic skills, and develops lasting mental maps of places and regions.     

A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management

In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data (sensor data, hydrological data and digital elevation models). We apply this approach to examine the micro-blogging text messages of the Twitter platform (tweets) produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near (up to 10 km) to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring.     

Recommending attractive thematic regions by semantic community detection with multi-sourced VGI data

Attractive regions can be detected and recommended by investigating users’ online footprints. However, social media data suffers from short noisy text and lack of a-priori knowledge, impeding the usefulness of traditional semantic modelling methods. Another challenge is the need for an effective strategy for the selection/recommendation of candidate regions. To address these challenges, we propose a comprehensive workflow which combines semantic and location information of social media data to recommend thematic urban regions to users with specific interests. This workflow is novel in: (1) developing a data-driven geographic topic modelling method which utilizes the co-occurrence patterns of self-explanatory semantic information to detect semantic communities; (2) proposing a new recommendation strategy with the consideration of region’s spatial scale. The workflow was implemented using a real-world dataset and evaluation conducted at three different levels: semantic representativeness, topic identification and recommendation desirability. The evaluation showed that the semantic communities detected were internally consistent and externally differentiable and that the recommended regions had a high degree of desirability. The work has demonstrated the effectiveness of self-explanatory semantic information for geographic topic modelling and highlighted the importance of including region spatial scale into the model for an effective region recommending strategy.     

数字城市框架下地理信息服务语义分析

构建数字城市服务公共平台,能够有效地解决异源异构数据共享的问题,实现地理信息互操作。该文以数字城市服务公共平台下的地理信息服务为例,基于本体论,利用属性枚举的语义表达方法构建地理信息服务本体,以明确且形式化的方法描述服务语义,力图解决地理信息服务语义共享的问题。最后给出数字城市公共平台部分地理信息服务的形式化语义描述,在Protégé软件中完成地理信息服务本体的构建,同时结合HermiT1.3.4实现推理过程,并以地理编码服务为例构建了一组本体结构。     

茂名市地名与地形关系

地形是自然环境中影响地名的一个重要地理要素,通过对茂名市88个镇和其下辖1 513个村地名的统计,结合地名志等文献资料中的地名来源,发现茂名市地名与地形的关系密切,且山地地区地名与地形关联性最强,丘陵地区次之,平原台地地区最小。地形影响地名的产生,地名也反映着居住地选择的趋向,显示了茂名的地名文化。通过对茂名市地名与地形各个要素之间关系的分析,得出了山区的聚落多选择建立在山谷中比较低平的区域;丘陵地区的居住地常选择在山坡和山谷、山间的低地;平原台地地区有较多地名与单座山有关。     

IS IT ROSLYN OR IS IT CICELY? REPRESENTATION AND THE AMBIGUITY OF PLACE

Representations or images of place are widely recognized as contributing to how people understand the places in which they live, work, and study. Increasingly they are being seen to contribute to the production of places as well. This paper addresses the relationships between representation and place as social processes by examining the blurring of Cicely, the Alaskan town in the television show Northern Exposure, and Roslyn, the town in Washington state where outdoor scenes of Northern Exposure are shot. Two main points are raised. First, the production of place-images by the entertainment industry has material repercussions for the residents of places. Second, the relationship between Roslyn and Cicely helps advance an epistemology of place that recognizes that the entire set of boundaries we use to define places limits our understanding of places as open, ambiguous, and, therefore, political social processes.