Assessing Resistance to Volunteered Geographic Information Reporting within Local Government

Using volunteered geographic information (VGI) as a cost‐saving environmental monitoring and public participation mechanism has been thought to be a proof of concept to assist environmental decision‐making with insufficient managerial resources. However, research literature also often found institutional resistance when implementing VGI as part of the decision‐making process. This research develops a conceptual framework as the foundation to conduct a cross‐case comparison for four empirical cases in recreation and invasive species monitoring, but general enough for other such case comparisons. The VGI Reporting Assessment Framework (VGI‐RAF) adopted aspects of the Decision Situation Assessment (DSA) framework as the foundation to address institutional flexibility, stakeholder dynamics, and technical considerations. Using a cross‐case comparison can accumulate deep observations from cases and increase validity of insights through comparing processes. Results show that eight aspects of the VGI‐RAF reveal the suitability of embedding voluntary information within existing institutional decision‐making processes. VGI‐RAF depicts considerations for: (1) existing participation arrangements, (2) institutional arrangements, (3) managers’ needs and stakeholder expectation, and (4) interaction mechanism and technical needs. The cross‐comparison results indicate that a public participation mechanism should be accompanied by VGI development to increase effectiveness. Institutional arrangements should match local government's information needs with participants’ expectations. Knowledge gaps between institutional information needs and volunteer knowledge should be filled through technology enhancements. For ease of implementation and to promote long‐term success, we recommend that volunteered information be connected with existing communication channels to minimize changes to existing information systems.     

Assessing the Accuracy of Volunteered Geographic Information arising from Multiple Contributors to an Internet Based Collaborative Project

The recent rise of neogeography and citizen sensing has increased the opportunities for the use of crowdsourcing as a means to acquire data to support geographical research. The value of the resulting volunteered geographic information is, however, often limited by concerns associated with its quality and the degree to which the contributing data sources may be trusted. Here, information on the quality of sources of volunteered geographic information was derived using a latent class analysis. The volunteered information was on land cover interpreted visually from satellite sensor images and the main focus was on the labeling of 299 sites by seven of the 65 volunteers who contributed to an Internet‐based collaborative project. Using the information on land cover acquired by the multiple volunteers it was shown that the relative, but not absolute, quality of the data from different volunteers could be characterized accurately. Additionally, class‐specific variations in the quality of the information provided by a single volunteer could be characterized by the analysis. The latent class analysis, therefore, was able to provide information on the quality of information provided on an inter‐ and intra‐volunteer basis.     

VGI数据与地形图数据的自动融合研究

随着科学技术的不断发展,志愿者地理信息（volunteered geographic information,VGI）已经成为地理空间数据中最为重要的来源之一。为了充分利用志愿者地理信息,需要进行VGI与传统地形图数据的匹配与融合。开发了一种全新的数据自动匹配与融合算法,其目的是将ATKIS道路网数据（由德国联邦测绘局所采集的官方数据）与AOSD数据（由大量志愿者携带定位仪器进行户外徒步或骑行所获取的轨迹数据）匹配并融合起来,从而丰富传统地理信息数据的内容,并实现数据的增值。考虑到ATKIS数据与AOSD数据在空间表达上的差异很大,所开发的算法包括了道路要素的智能化分割、道路要素匹配、道路网数据融合以及融合后道路网内部要素间的匹配运算与数据集成等4个过程。大量实地数据的测试结果表明,该算法具有匹配成功率高、准确率高、运算速度快等优点。     

Conflation of OpenStreetMap and Mobile Sports Tracking Data for Automatic Bicycle Routing

This article investigates how workout trajectories from a mobile sports tracking application can be used to provide automatic route suggestions for bicyclists. We apply a Hidden Markov Model (HMM)‐based method for matching cycling tracks to a “bicycle network” extracted from crowdsourced OpenStreetMap (OSM) data, and evaluate its effective differences in terms of optimal routing compared with a simple geometric point‐to‐curve method. OSM has quickly established itself as a popular resource for bicycle routing; however, its high‐level of detail presents challenges for its applicability to popularity‐based routing. We propose a solution where bikeways are prioritized in map‐matching, achieving good performance; the HMM‐based method matched correctly on average 94% of the route length. In addition, we show that the extremely biased nature of the trajectory dataset, which is typical of volunteered user‐generated data, can be of high importance in terms of popularity‐based routing. Most computed routes diverged depending on whether the number of users or number of tracks was used as an indicator of popularity, which may imply varying preferences among different types of cyclists. Revising the number of tracks by diversity of users to surmount local biases in the data had a more limited effect on routing.     

Combining Geo‐SOM and Hierarchical Clustering to Explore Geospatial Data

Geo‐SOM is a useful geovisualization technique for revealing patterns in spatial data, but is ineffective in supporting interactive exploration of patterns hidden in different Geo‐SOM sizes. Based on the divide and group principle in geovisualization, the article proposes a new methodology that combines Geo‐SOM and hierarchical clustering to tackle this problem. Geo‐SOM was used to “divide” the dataset into several homogeneous subsets; hierarchical clustering was then used to “group” neighboring homogeneous subsets for pattern exploration in different levels of granularity, thus permitting exploration of patterns at multiple scales. An artificial dataset was used for validating the method's effectiveness. As a case study, the rush hour motorcycle flow data in Taipei City, Taiwan were analyzed. Compared with the best result generated solely by Geo‐SOM, the proposed method performed better in capturing the homogeneous zones in the artificial dataset. For the case study, the proposed method discovered six clusters with unique data and spatial patterns at different levels of granularity, while the original Geo‐SOM only identified two. Among the four hierarchical clustering methods, Ward's clustering performed the best in pattern discovery. The results demonstrated the effectiveness of the approach in visually and interactively exploring data and spatial patterns in geospatial data.     

志愿者微信地震灾情获取研究

针对现有的志愿者灾情信息获取方式进行了分析,将其分为主动式和被动式两大类。比较发现主动式获取不管是在灾情信息获取的精确度还是在数据处理的难度方面都存在明显的优势。在此基础上研究了基于微信的志愿者地震灾情获取框架与流程。融合地震灾情信息语义分析与公众口语化表达特点将地震灾情信息分为5类,并设计出适用于专业志愿者与普通公众进行灾情上报的灾情语义分类。最后,通过微信公众平台结合ArcGIS Server设计并实现了基于微信的志愿者地震灾情获取系统。     

Support Vector Machine for Spatial Variation

Large, multivariate geographic datasets have been used to characterize geographic space with the help of spatial data mining tools. In our study, we explore the sufficiency of the Support Vector Machine (SVM), a popular machine‐learning technique for unsupervised classification and clustering, to help recognize hidden patterns in a college admissions dataset. Our college admissions dataset holds over 10,000 students applying to an undisclosed university during one undisclosed year. Students are qualified almost exclusively by their standardized test scores and school records, and a known admissions decision is rendered based on these criteria. Given that the university has a number of political, social and geographic econometric factors in its admissions decisions, we use SVM to find implicit spatial patterns that may favor students from certain geographic regions. We first explore the characteristics of the applicants in the college admissions case study. Next, we explain the SVM technique and our unique ‘threshold line’ methodology for both discrete (regional) and continuous (k‐neighbors) space. We then analyze the results of the regional and k‐neighbor tests in order to respond to the methodological and geographic research questions.     

An analysis of movement patterns between zones using taxi GPS data

The discovery of zones and people's movement patterns supports a better understanding of modern cities and enables a more comprehensive strategy for urban planning. This article proposes a modified method based on previous research to simultaneously discover people's zones and movement patterns, called movement patterns between functional zones (MPFZ). The method attempts to take full advantage of taxi GPS data to identify MPFZs by merging the movement traces satisfying the merging conditions. Considering movement directions, movement numbers and the adjacent constraints that consist of spatial relationship and attribute features, the merging conditions limit the movement traces to be merged. The new MPFZs are discovered by an iteration process and are measured by the following three evaluation indices: v‐value, a‐value and c‐value, which represent coverage, accuracy and their trade‐off. Using a real‐world taxi dataset of Beijing, 24 new MPFZs are discovered, which have higher v‐, a‐ and c‐values than the unmerged MPFZs. The results of the real‐world dataset experiment show that the proposed approach is effective and efficient. The proposed method can also be applied to other types of transportation data and regions by adjusting the dataset utilized and controlling the iteration process.     

Using control data to determine the reliability of volunteered geographic information about land cover

There is much interest in using volunteered geographic information (VGI) in formal scientific analyses. This analysis uses VGI describing land cover that was captured using a web-based interface, linked to Google Earth. A number of control points, for which the land cover had been determined by experts allowed measures of the reliability of each volunteer in relation to each land cover class to be calculated. Geographically weighted kernels were used to estimate surfaces of volunteered land cover information accuracy and then to develop spatially distributed correspondences between the volunteer land cover class and land cover from 3 contemporary global datasets (GLC-2000, GlobCover and MODIS v.5). Specifically, a geographically weighted approach calculated local confusion matrices (correspondences) at each location in a central African study area and generated spatial distributions of user's, producer's, portmanteau, and partial portmanteau accuracies. These were used to evaluate the global datasets and to infer which of them was 'best’ at describing Tree cover at each location in the study area. The resulting maps show where specific global datasets are recommended for analyses requiring Tree cover information. The methods presented in this research suggest that some of the concerns about the quality of VGI can be addressed through careful data collection, the use of control points to evaluate volunteer performance and spatially explicit analyses. A research agenda for the use and analysis of VGI about land cover is outlined.     

Knowledge Discovery in the Environmental Sciences: Visual and Automatic Data Mining for Radon Problems in Groundwater

Efficiently exploring a large dataset with the aim of forming a hypothesis is one of the main challenges in environmental research. The exploration of georeferenced environmental data is usually performed by established statistical methods. This paper presents an alternative approach. The aim of this study was to see if a visual data mining system and an integrated visual‐automatic data mining system could be used for data exploration for a particular environmental problem: the occurrence of radon in groundwater. In order to demonstrate this, two data mining systems were built, one consisting of visualisations and the other including an automatic data mining method – a Self‐Organising Map (SOM). The systems were designed for exploration of a large multidimensional dataset representing wells in Stockholm County.     

A Spatial Analysis System for Integrating Data,Methods and Models on Environmental Risks and Health Outcomes

Integrating data on health outcomes with methods of disease mapping and spatially explicit models of environmental contaminants are important aspects of environmental health surveillance. In this article, we describe a modular, web‐based spatial analysis system that uses GIS, spatial analysis methods and software services delivered over computer networks to achieve this end. The Environmental Health Surveillance System (EHSS) is a prototype system that is designed to serve three purposes: a secure environment for producing maps of disease outcomes from individual‐level data while preserving privacy; an automated process of linking environmental data, environmental models, and GIS tasks like geocoding for the purposes of estimating individual exposures to environmental contaminants; and mechanisms to visualize the spatial patterns of disease outcomes via Web‐based mapping interfaces and interactive tools like Google Earth.     

地理信息志愿者信誉度评估的版本相似度模型——以面目标为例

针对自发地理信息中存在大量恶意、虚假、低质量数据,提出了一种基于版本相似度的VGI志愿者信誉度计算模型。该模型将每个志愿者对某个地理空间目标的一次编辑结果定义为一个版本,当其他用户编辑该目标版本时,通过计算版本相似度来获得其他用户对该志愿者这个目标版本的支持度;然后通过计算其他贡献者对某志愿者所贡献的每个目标的支持度,对所有目标的支持度进行加权平均获得该志愿者的综合信誉度。其中版本相似度综合目标空间和属性相似性的主要因素进行加权计算。然后以面目标为例阐述了本文志愿者信誉度计算过程。为了验证本文信誉度计算模型的合理性,笔者采用德国柏林OpenStreetMap数据进行试验,试验表明通过本文模型计算获得的用户信誉度与其贡献的要素质量总体上正相关。     

Modeling aggregated expertise of user contributions to assess the credibility of OpenStreetMap features

The emergence of volunteered geographic information (VGI) during the past decade has fueled a wide range of research and applications. The assessment of VGI quality and fitness‐of‐use is still a challenge because of the non‐standardized and crowdsourced data collection process, as well as the unknown skill and motivation of the contributors. However, the frequent approach of assessing VGI quality against external data sources using ISO quality standard measures is problematic because of a frequent lack of available external (reference) data, and because for certain types of features, VGI might be more up‐to‐date than the reference data. Therefore, a VGI‐intrinsic measure of quality is highly desirable. This study proposes such an intrinsic measure of quality by developing the concept of aggregated expertise based on the characteristics of a feature's contributors. The article further operationalizes this concept and examines its feasibility through a case study using OpenStreetMap (OSM). The comparison of model OSM feature quality with information from a field survey demonstrates the successful implementation of this novel approach.     

Visualising Crime Clusters in a Space‐time Cube: An Exploratory Data‐analysis Approach Using Space‐time Kernel Density Estimation and Scan Statistics

For an effective interpretation of spatio‐temporal patterns of crime clusters/hotspots, we explore the possibility of three‐dimensional mapping of crime events in a space‐time cube with the aid of space‐time variants of kernel density estimation and scan statistics. Using the crime occurrence dataset of snatch‐and‐run offences in Kyoto City from 2003 to 2004, we confirm that the proposed methodology enables simultaneous visualisation of the geographical extent and duration of crime clusters, by which stable and transient space‐time crime clusters can be intuitively differentiated. Also, the combined use of the two statistical techniques revealed temporal inter‐cluster associations showing that transient clusters alternatively appeared in a pair of hotspot regions, suggesting a new type of “displacement” phenomenon of crime. Highlighting the complementary aspects of the two space‐time statistical approaches, we conclude that combining these approaches in a space‐time cube display is particularly valuable for a spatio‐temporal exploratory data analysis of clusters to extract new knowledge of crime epidemiology from a data set of space‐time crime events.     

Identifying stops from mobile phone location data by introducing uncertain segments

Identifying stops is a primary step in acquiring activity‐related information from mobile phone location data to understand the activity patterns of individuals. However, signal jumps in mobile phone location data may create “fake moves,” which will generate fake activity patterns of “stops‐and‐moves.” These “fake moves” share similar spatiotemporal features with real short‐distance moves, and the stops and moves of trajectories (SMoT), which is the most extensively used stop identification model, often fails to distinguish them when the dataset has coarse temporal resolution. This study proposes the stops, moves, and uncertainties of trajectories (SMUoT) model to address this issue by introducing uncertain segment analysis to distinguish “fake moves” and real short‐distance moves. A real mobile phone location dataset collected in Shenzhen, China is used to evaluate the performance of SMUoT. We find that SMUoT improves the performance (i.e., 15 and 19% increase in accuracy and recall rate for a one‐hour temporal resolution dataset, respectively) of stop identification and exhibits high robustness to parameter settings. With a better reliability of “stops‐and‐moves” pattern identification, the proposed SMUoT can benefit various individual activity‐related research based on mobile phone location data for many fields, such as urban planning, traffic analysis, and emergency management.     

Geospatial Human‐environment Simulation through Integration of Massive Multiplayer Online Games and Geographic Information Systems

This article reports on the initial development of a generic framework for integrating Geographic Information Systems (GIS) with Massive Multi‐player Online Gaming (MMOG) technology to support the integrated modeling of human‐environment resource management and decision‐making. We review Web 2.0 concepts, online maps, and games as key technologies to realize a participatory construction of spatial simulation and decision making practices. Through a design‐based research approach we develop a prototype framework, “GeoGame”, that allows users to play board‐game‐style simulations on top of an online map. Through several iterations we demonstrate the implementation of a range of design artifacts including: real‐time, multi‐user editing of online maps, web services, game lobby, user‐modifiable rules and scenarios building, chat, discussion, and market transactions. Based on observational, analytical, experimental and functional evaluations of design artifacts as well as a literature review, we argue that a MMO GeoGame‐framework offers a viable approach to address the complex dynamics of human‐environmental systems that require a simultaneous reconciliation of both top‐down and bottom‐up decision making where stakeholders are an integral part of a modeling environment. Further research will offer additional insight into the development of social‐environmental models using stakeholder input and the use of such models to explore properties of complex dynamic systems.     

Discovering Spatial Interaction Communities from Mobile Phone Data

In the age of Big Data, the widespread use of location‐awareness technologies has made it possible to collect spatio‐temporal interaction data for analyzing flow patterns in both physical space and cyberspace. This research attempts to explore and interpret patterns embedded in the network of phone‐call interaction and the network of phone‐users’ movements, by considering the geographical context of mobile phone cells. We adopt an agglomerative clustering algorithm based on a Newman‐Girvan modularity metric and propose an alternative modularity function incorporating a gravity model to discover the clustering structures of spatial‐interaction communities using a mobile phone dataset from one week in a city in China. The results verify the distance decay effect and spatial continuity that control the process of partitioning phone‐call interaction, which indicates that people tend to communicate within a spatial‐proximity community. Furthermore, we discover that a high correlation exists between phone‐users’ movements in physical space and phone‐call interaction in cyberspace. Our approach presents a combined qualitative‐quantitative framework to identify clusters and interaction patterns, and explains how geographical context influences communities of callers and receivers. The findings of this empirical study are valuable for urban structure studies as well as for the detection of communities in spatial networks.     

A Review of Sampling Effects and Response Bias in Internet Participatory Mapping (PPGIS/PGIS/VGI)

Global interest in participatory mapping described as public participation GIS (PPGIS), participatory GIS (PGIS), and volunteered geographic information (VGI) continues to grow, but systematic study of spatial data quality and sampling effects is limited. This article provides a review and meta‐analysis of Internet‐based PPGIS studies conducted during the period 2006–2015 (n=26) to answer the following research questions: (1) How does mapping effort, as a proxy measure for spatial data quality, differ by sampling group? (2) Does the purpose and context of PPGIS influence mapping results? (3) What is the potential for mapping bias through sampling design? (4) Given the results, what should be the focus of future PPGIS research? Mapping effort was highest in sampling groups whose livelihoods were closely related to the purpose of the study, there was greater mapping effort in household sampling groups compared to volunteer groups, and participant domicile had strong effects on mapped results through spatial discounting. The use of online Internet panels provides higher response rates but lower spatial data quality. Future research should focus on increasing sampling response rates, assessing social trade‐offs using alternative spatial weighting schemes, and examining the capacity of the public to select land use alternatives as a complement to traditional expert‐driven planning systems.     

Limitations of Majority Agreement in Crowdsourced Image Interpretation

Crowdsourcing can efficiently complete tasks that are difficult to automate, but the quality of crowdsourced data is tricky to evaluate. Algorithms to grade volunteer work often assume that all tasks are similarly difficult, an assumption that is frequently false. We use a cropland identification game with over 2,600 participants and 165,000 unique tasks to investigate how best to evaluate the difficulty of crowdsourced tasks and to what extent this is possible based on volunteer responses alone. Inter‐volunteer agreement exceeded 90% for about 80% of the images and was negatively correlated with volunteer‐expressed uncertainty about image classification. A total of 343 relatively difficult images were independently classified as cropland, non‐cropland or impossible by two experts. The experts disagreed weakly (one said impossible while the other rated as cropland or non‐cropland) on 27% of the images, but disagreed strongly (cropland vs. non‐cropland) on only 7%. Inter‐volunteer disagreement increased significantly with inter‐expert disagreement. While volunteers agreed with expert classifications for most images, over 20% would have been mis‐categorized if only the volunteers’ majority vote was used. We end with a series of recommendations for managing the challenges posed by heterogeneous tasks in crowdsourcing campaigns.     

面向多种网络带宽环境的远程教学视频传输技术研究

面向网络教育教学视频传输的需求，对视频传输所涉及的视频编码、网络带宽自适应等一些关键技术进行了研究。