基于XML Schema的多源地名词典集成方法

目前,我国已经构建大量不同级别、面向不同应用的地名词典,网络大众化地名服务成为地名词典的必然发展趋势。该文提出一种基于XML Schema的多源异构地名词典集成方法,以XML Schema对地名词典进行数据描述,采用XSLT数据转换方法,运用MapForce软件,快速进行地名词典的数据结构映射,能够有效解决地名词典的跨平台及数据类型不统一问题。     

Thematic signatures for cleansing and enriching place-related linked data

There has been significant progress transforming semi-structured data about places into knowledge graphs that can be used in a wide variety of geographic information systems such as digital gazetteers or geographic information retrieval systems. For instance, in addition to information about events, actors, and objects, DBpedia contains data about hundreds of thousands of places from Wikipedia and publishes it as Linked Data. Repositories that store data about places are among the most interlinked hubs on the Linked Data cloud. However, most content about places resides in unstructured natural language text, and therefore it is not captured in these knowledge graphs. Instead, place representations are limited to facts such as their population counts, geographic locations, and relations to other entities, for example, headquarters of companies or historical figures. In this paper, we present a novel method to enrich the information stored about places in knowledge graphs using thematic signatures that are derived from unstructured text through the process of topic modeling. As proof of concept, we demonstrate that this enables the automatic categorization of articles into place types defined in the DBpedia ontology (e.g., mountain) and also provides a mechanism to infer relationships between place types that are not captured in existing ontologies. This method can also be used to uncover miscategorized places, which is a common problem arising from the automatic lifting of unstructured and semi-structured data.     

数字地名词典中的类型表达和管理

在数字地名词典中恰当地表达和管理地名的类型知识,有助于数字地名词典快速、有效地处理地名相关查询.为此,该文首先从数字地名词典的应用需求出发,分析类型在数字地名词典中的作用,进而设计一个地名类型本体模型.该模型表达了地名类型之间的继承关系以及对空间关系的约束.在本体模型基础上,提出了相应的查询处理策略并进行了系统实现.     

GeoCorpora: building a corpus to test and train microblog geoparsers

In this article, we present the GeoCorpora corpus building framework and software tools as well as a geo-annotated Twitter corpus built with these tools to foster research and development in the areas of microblog/Twitter geoparsing and geographic information retrieval. The developed framework employs crowdsourcing and geovisual analytics to support the construction of large corpora of text in which the mentioned location entities are identified and geolocated to toponyms in existing geographical gazetteers. We describe how the approach has been applied to build a corpus of geo-annotated tweets that will be made freely available to the research community alongside this article to support the evaluation, comparison and training of geoparsers. Additionally, we report lessons learned related to corpus construction for geoparsing as well as insights about the notions of place and natural spatial language that we derive from application of the framework to building this corpus.     

Representing the visibility structure of a polyhedral terrain through a horizon map

Abstract

We present a model for describing the visibility of a polyhedral terrain from a fixed viewpoint, based on a collection of nested horizons. We briefly introduce the concepts of mathematical and digital terrain models, and some background notions for visibility problems on terrains. Then, we define horizons on a polyhedral terrain, and introduce a visibility model, that we call the horizon map. We present a construction algorithm and a data structure for encoding the horizon map, and show how it can be used for solving point visibility queries with respect to a fixed viewpoint.     

广义地名及其本体研究

提出广义地名的概念(指可以与地球表面特定位置或范围相关联的一段文本),基于广义地名组织的文本形式的空间信息通常更符合人们对于地理空间知识的表达。探讨广义地名的本体,并给出相应的概念模型和逻辑模型,从而有助于广义地名数据库以及智能化广义地名服务的实现。该研究主要从广义地名的发生学、描述对象、地理范围、信息团体和语境引起的二义性等方面描述广义地名本体,并用UML语言建立便于在信息系统中实现的逻辑模型。     

Cross-Linguistic Research on Landscape Categories Using GEOnet Names Server Data: A Case Study for Indonesia and Malaysia

This article explores how toponym databases (gazetteers) can be used to examine the ethnophysiography hypothesis, which states that people from different language groups/cultures have different ways of conceptualizing landscape. Toponyms of two classes of eminences, mountain and hill, in Malaysia and Indonesia where the dominant languages are highly cognate were extracted from the National Geospatial-Intelligence Agency (NGA) GEOnet Names Server (GNS) and analyzed using a heuristics-based geomorphometry approach. The result revealed that gunung and bukit, the main terms in Malay or Indonesian languages similar to mountain and hill in English, are used differently in the two countries: Bukit and gunung are used interchangeably for eminence features in Indonesia regardless of size (area extent), but the use of gunung is more strict for Malaysia to refer to larger eminence features. Furthermore, in Indonesia, the NGA GNS can readily be used to identify generic terms in local languages and dialects; we show that in some cases, size of eminence is distinguished with different generic terms and that spatial proximity might affect which term is used. The result suggests that NGA GNS is a promising set of data to explore ethnophysiography research questions, and can be a valuable exploratory tool for guiding detailed ethnophysiography work.     

Machine learning for cross-gazetteer matching of natural features

ABSTRACT

Defining and identifying duplicate records in a dataset is a challenging task which grows more complex when the modeled entities themselves are hard to delineate. In the geospatial domain, it may not be clear where a mountain, stream, or valley ends and begins, a problem carried over when such entities are catalogued in gazetteers. In this paper, we take two gazetteers, GeoNames and SwissNames3D, and perform matching – identifying records in each that are about the same entity – across a sample of natural feature records. We first perform rule-based matching, establishing competitive results, then apply machine learning using Random Forests, a method well-suited to the matching task. We report on the performance of a wider array of matching features than has been previously studied, including domain-specific ones such as feature type, land cover class, and elevation. Our results show an increase in performance using machine learning over rules, with a notable performance gain from considering feature types, but negligible gains from other specialized matching features. We argue that future work in this area should strive to be more reproducible and report results on a realistic testing pipeline including candidate selection, feature extraction, and classification.     

Efficient methods for Isoline extraction from a TIN

Abstract

A data structure is presented to store a triangulated irregular network digital elevation model, from which isolines (contour lines) can be extracted very efficiently. If the network is based on n points, then for any elevation, the isolines can be obtained in O (logn + k) query time, where k is the number of line segments that form the isolines. This compares favourably with O(n) time by straightforward computation. When a structured representation of the isolines is needed, the same query time applies. For a fully topological representation (with adjacency), the query requires additional O(c log c) or O(c log logo) time, where c is the number of connected components of isolines. In all three cases, the required data structure has only linear size.     

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.     

Conceptualising the geographic world: the dimensions of negotiation in crowdsourced cartography

In crowdsourced cartographic projects, mappers coordinate their efforts through online tools to produce digital geospatial artefacts, such as maps and gazetteers, which were once the exclusive territory of professional surveyors and cartographers. In order to produce meaningful and coherent data, contributors need to negotiate a shared conceptualisation that defines the domain concepts, such as road, building, train station, forest and lake, enabling the communication of geographic knowledge. Considering the OpenStreetMap Wiki website as a case study, this article investigates the nature of this negotiation, driven by a small group of mappers in a context of high contribution inequality. Despite the apparent consensus on the conceptualisation, the negotiation keeps unfolding in a tension between alternative representations, which are often incommensurable, i.e., hard to integrate and reconcile. In this study, we identify six complementary dimensions of incommensurability that recur in the negotiation: (1) ontology, (2) cartography, (3) culture and language, (4) lexical definitions, (5) granularity, and (6) semantic overload and duplication.     

Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing,China

ABSTRACT

One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM_2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM_2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM_2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM_2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R² of 0.93 and root mean square error of 9.64 μg/m³. The results indicated that the multi-stage PM_2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.     

Supporting multi-resolution out-of-core rendering of massive LiDAR point clouds through non-redundant data structures

In recent years, the evolution and improvement of LiDAR (Light Detection and Ranging) hardware has increased the quality and quantity of the gathered data, making the storage, processing and management thereof particularly challenging. In this work we present a novel, multi-resolution, out-of-core technique, used for web-based visualization and implemented through a non-redundant, data point organization method, which we call Hierarchically Layered Tiles (HLT), and a tree-like structure called Tile Grid Partitioning Tree (TGPT). The design of these elements is mainly focused on attaining very low levels of memory consumption, disk storage usage and network traffic on both, client and server-side, while delivering high-performance interactive visualization of massive LiDAR point clouds (up to 28 billion points) on multiplatform environments (mobile devices or desktop computers). HLT and TGPT were incorporated and tested in ViLMA (Visualization for LiDAR data using a Multi-resolution Approach), our own web-based visualization software specially designed to work with massive LiDAR point clouds.     

Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city

ABSTRACT

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information.     

Permits to burn: weeds,slow violence,and the extractive future of northern Australia

ABSTRACT

This essay narrates the ‘slow violence’, or creeping environmental harms taking place within contemporary environmental governance. It centres on a tall, dense and highly flammable introduced pasture species Gamba grass (Andropogon gayanus), which was listed as a weed across north Australian jurisdictions in 2008. Since this time, it has continued to expand its reach across the Northern Territory (NT). With a potential invasion range of over 380,000?sqkm², this grass is a serious threat to many more-than-human worlds in the north, including Indigenous-led and Indigenous-owned environmental service economies and multimillion-dollar projects engaged in savanna fire management for carbon credits. Drawing upon fieldwork and interviews with a range of public servants, landholders and researchers in the NT between 2015 and 2018, this essay demonstrates how environmental governance is being undermined through specific institutions and practices. Through an ethnographic reading of weed management documents, including several legal permits to grow Gamba grass within the NT’s ‘eradication zone’, this essay narrates the diverse threads of a pressing ‘slow’ disaster. The unfolding story of Gamba grass, we suggest, is instructive for those seeking to understand the present and future of resource extraction or ‘extractivism’ in Australia and elsewhere.     

Rewriting the book of the city: on old signs,new technologies,and reinventing Adelaide

Can a sense of ownership over constantly changing urban places be generated through pedestrian exploration and everyday digital media practice? This paper begins with the author’s visit to the city of Adelaide, where a chance encounter – an intriguing sign painted on the wall of a city building – led to a hunt through the city for ghost signs, the remnants of old hand-painted advertisements. This spontaneous activity, combined with the online exploration and image sharing that followed, generated a renewed feeling of connection to, and ownership over, an erstwhile familiar locale. The experience is analysed through the work of theorists investigating change and belonging in modern cities including Guy Debord, Doreen Massey, Michel de Certeau, Andreas Huyssen, and Mark Crinson, as well as the contemporary growth in both politically inspired urban exploring (“Urbex” or “place hacking”) and the digital documentation and sharing of ghost signs and other urban ephemera.     

“GEOGRAPHY IS TWINNED WITH DIVINITY”: THE LAUDIAN GEOGRAPHY OF PETER HEYLYN

ABSTRACT. This critical history of geography looks to the political concepts that historical actors held and analyzes the incorporation of these concepts into geography. Peter Heylyn, who politicized his geographical books Microcosmus (1621) and, still more, Cosmographie (1657), followed William Laud's characteristic brand of High Church Anglicanism, avowedly hostile both to Roman Catholicism and to Calvinist forms of Protestantism, while upholding an ideal of the Church of England as both independent and apostolic. Further, Laudians were stalwart defendants of monarchy as a divine institution. This Laudian vision of church and state informed Heylyn's geographical works, which goes against a received wisdom that they are divorced from his polemical historical, political, and theological tracts. We thus recover the politics of early modern geography as contemporaries might have understood them.     

Gradients,Property Templates,and Land Use Change

The scarcity of historical individual-level data makes understanding historic land use decisions and the influence of physiochemical gradients on these decisions difficult. Here, we present several measures of early property agricultural quality based on commonly available data, including elevation and soil type. These analyses demonstrate the influence of physiochemical gradients on initial land division patterns in the Gywnns Falls watershed (Baltimore, Maryland). Moreover, we examine the influence of the template created by early property decisions on continuing human-driven landscape dynamics. This influence is illustrated by 1900-era forest cover patterns, park locations, and modern transportation networks.