Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks

In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R ²) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R ² ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.     

Computing the semantic similarity of geographic terms using volunteered lexical definitions

Volunteered geographic information (VGI) is generated by heterogenous ‘information communities’ that co-operate to produce reusable units of geographic knowledge. A consensual lexicon is a key factor to enable this open production model. Lexical definitions help demarcate the boundaries of terms, forming a thin semantic ground on which knowledge can travel. In VGI, lexical definitions often appear to be inconsistent, circular, noisy and highly idiosyncratic. Computing the semantic similarity of these ‘volunteered lexical definitions’ has a wide range of applications in GIScience, including information retrieval, data mining and information integration. This article describes a knowledge-based approach to quantify the semantic similarity of lexical definitions. Grounded in the recursive intuition that similar terms are described using similar terms, the approach relies on paraphrase-detection techniques and the lexical database WordNet. The cognitive plausibility of the approach is evaluated in the context of the OpenStreetMap (OSM) Semantic Network, obtaining high correlation with human judgements. Guidelines are provided for the practical usage of the approach.     

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.     

Fine-resolution population mapping using OpenStreetMap points-of-interest

Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.     

The Technology of Religion: Mapping Religious Cyberscapes

This article combines geographical studies of both the Internet and religion in an analysis of where and how a variety of religious practices are represented in geotagged Web content. This method provides needed insight into the geography of virtual expressions of religion and highlights the mutually constitutive, and at times contradictory, relationship between the virtual and material dimensions of religious expression. By using the spatialities of religious practice and contestation as an example, this article argues that mappings of virtual representations of material practices are important tools for understanding how online activities simultaneously represent and reproduce the material world.     

Ripe for the picking? Dataset maturity assessment based on temporal dynamics of feature definitions

Map databases traditionally capture snapshot representations of the world following strict data collection and representation guidelines. The content of these map databases is often assessed using data quality metrics focusing on accuracy, completeness and consistency. The success of volunteered geographic information, supporting evolving representations of the world based on fluid guidelines, has rendered these measures insufficient. In this paper, we address the need to capture the variability in quality of a map database. We propose a new spatial data quality measure – dataset maturity – enabling assessment of the database based on temporal trends in feature definitions, specifically geometry-type definitions. The proposed measure can be (1) efficiently used to identify feature definition patterns reflecting community consensus that could be formalised in community guidelines and (2) deployed to identify regions that would benefit from increased editorial activity to achieve greater map homogeneity. We demonstrate the measure based on the content of the OpenStreetMap database in four regions of the world and show how the proposed dataset maturity measure captures a distinct quality of the datasets, distinct to data completeness and consistency.     

信息地理学学科体系与发展战略要点

信息时代的到来极大地促进了地理科学的发展,地理科学的研究已从传统的自然地理空间、人文地理空间拓展到了信息地理空间,催生了信息地理学的发展,并逐渐形成了地理遥感科学、地理信息科学和地理数据科学3个分支学科。在《中国学科及前沿领域发展战略研究（2021—2035）》地理科学的学科规划背景下,本文梳理了信息地理学的形成、定义和学科体系,重点阐述了信息地理学的学科发展战略布局、优先领域发展目标和重点方向。以期本文有助于促进遥感、地理信息科学与技术的发展和应用回归地理科学,进一步强化地理科学研究,使其更加系统化、科学化和现代化,促进地理科学的整体发展。     

Disciplinary structure and development strategy of information geography in China

Journal of Geographical Sciences - The advent of the information era has resulted in exceptional advances in geographic science. The domain of geographic science has expanded from traditional...     

Location‐based social media behavior and perception: Views of university students

Location‐based social media (LBSM), a specific type of volunteered geographic information (VGI), is increasingly being used as a spatial data source for researchers in geography and related disciplines. Many questions, though, have been raised about VGI data in terms of its quality and its contributors. While a number of studies have explored users’ demographics and motivations for contribution to explicitly geographic forms of VGI, such as OpenStreetMap and Wikimapia, few have focused on these aspects with implicitly geographic forms of VGI, such as LBSM (for example, Twitter and Instagram). This study, through use of an online survey, specifically assesses the LBSM behavior and perceptions of 253 university students, noting differences found in gender, race, and academic standing. We find that the greatest differences are those between males and females, rather than through race or academic standing, and LBSM appears less biased than other forms of VGI.     

A citizen data-based approach to predictive mapping of spatial variation of natural phenomena

The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (B_cont(0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the field-observed distribution of R. bieti. B_cont(0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (?0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.     

Geographic information systems and health: An educational resource

Abstract

The use of geographic information systems to analyze spatial dimensions of health care and disease ecology is becoming a realistic prospect for investigators in the social sciences. This review of the literature, bringing together a diverse collection of professional and academic journals, can be grouped into four categories: potential, cautionary, preliminary, and application. Enough references have been collected and reviewed to provide instructors with material for a classroom unit about 1) using GIS in a medical geography class: 2) medical applications in a GIS class; or 3) using GIS in classes which have a health and disease component.     

A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks

Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns.     

Digital Data-Centric Geography: Implications for Geography's Frontier

The debate regarding geographic information systems (GIS) as tool, toolbox, or science still lingers in geography departments and among geographers. Analysis of geographic information is a vital component of decision making among business, governments, researchers, and academics. GIS users, geographers and nongeographers alike, use and benefit from problem-solving methods in numerous fields and contexts, making the use of GIS and the core competencies associated with using GIS a topic of intense debate. Complicating this ongoing discussion is the rise of data-centric approaches to research in geography that further expand the capabilities of spatial analysis and add to the expected knowledge of a GIS user and analyst. Building on a panel discussion at the 2016 American Association of Geographers (AAG) annual meeting, as well as informal dialogues on Twitter and other social media platforms that navigate this issue in academics and industry, this article explores how skills in research computing and programming operate in geography and GIS, especially given the rise of data-centric approaches to research in these realms. Some topics, like the costs and benefits of open and closed source software, are familiar from previous discussions in geography and GIS. Others, though, like the reward structures and recognition for computing skills or programming ability, have not been widely considered given the current landscape.     

Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data

ABSTRACT

We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization.     

Internet-Based GIS in an Inductive Learning Environment: A Case Study of Ninth-Grade Geography Students

Abstract

This article reports the results of a qualitative case study that explored the use of Internet-based geographic information systems (IGIS) with ninth-grade geography students. The students worked in teams to examine the geography of Africa using IGIS resources with the instructional goal of developing a presentation on a significant issue facing one region or country. The purpose of this study was to explore the use of IGIS as a tool for integrating geospatial technologies into ninth-grade geography curriculum and instruction within an inductive learning environment. The findings for this study indicate that IGIS can be a successful tool for geography education in an inductive learning environment. Based on qualitative data collected through classroom observations, focus group interviews with students, student work samples, and a teacher journal, two assertions are offered: students perceived expanded freedom as a positive aspect of this IGIS project and IGIS projects such as this can lead to gains in students' cultural awareness and empathy for distant others.     

A review of volunteered geographic information quality assessment methods

With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. This phenomenon is known as volunteered geographic information (VGI). During the past decade VGI has been used as a data source supporting a wide range of services, such as environmental monitoring, events reporting, human movement analysis, disaster management, etc. However, these volunteer-contributed data also come with varying quality. Reasons for this are: data is produced by heterogeneous contributors, using various technologies and tools, having different level of details and precision, serving heterogeneous purposes, and a lack of gatekeepers. Crowd-sourcing, social, and geographic approaches have been proposed and later followed to develop appropriate methods to assess the quality measures and indicators of VGI. In this article, we review various quality measures and indicators for selected types of VGI and existing quality assessment methods. As an outcome, the article presents a classification of VGI with current methods utilized to assess the quality of selected types of VGI. Through these findings, we introduce data mining as an additional approach for quality handling in VGI.     

How block density and typology affect urban vitality: an exploratory analysis in Shenzhen,China

Recent urban transformations in China have led to critical reflections on the low-quality urban expansion in the previous decades and called for the making of vital and lively urban places. To date, limited research has been devoted to empirically testing the relationship between urban design, urban morphology, and urban vitality in Chinese cities. This paper employs new urban data and analytical methods and explores the relationship between urban morphology and urban vitality using regression models. Shenzhen, one of the largest and fastest growing cities in China, is selected as the case study. The regression analysis focuses on two morphological factors, density and typology, while controlling for the accessibility, functional mix, and size of individual blocks. The presence of small catering businesses is used as a proxy for urban vitality. The analysis suggests that both typology and density matter for urban vitality, with typology playing a more important role. More specifically, “block” and “strip” types tend to show significant positive effects on urban vitality. The implications for urban planning and design practices are discussed.     

Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining

This paper aims to qualify the behaviour of contributors to OpenStreetMap (OSM), a volunteered geographic information (VGI) project, through a multigraph approach. The main purpose is to reproduce contributor’s interactions in a more comprehensive way. First, we define a multigraph that combines existing spatial collaboration networks from the literature with new graphs that illustrate collaboration based on specific aspects of the VGI modes of contribution through semantics, geometry and topology. Indeed, the ways that contributors interact with one another through editing, completion, or even consumption may provide additional information on each user’s operation mode and therefore, on the quality of the contributed data. Social collaborations drawn from indirect criteria – for example, comparisons between contributors’ activity areas – can also be contemplated under another network. Second, the resulting multigraph is analysed using data mining approaches to characterise individuals and identify behavioural groups. The implementation of a multiplex network based on an OSM data sample and an initial analysis make it possible to identify useful behaviours for data qualification. The initial results characterise some contributors as pioneers, moderators and truthful contributors, according to their special roles in the graphs. Mapping elements that include these contributors’ participation are likely to be reliable data     

SMT: A SPATIAL MAPPING TOOL FOR STATISTICAL DATA SET

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