VGI Data Research Articles

Challenges and Issues in Spatial Data Infrastructure (SDI) Development in Iraq

This paper addresses the nature of Spatial Data Infrastructure (SDI), considered as one of the most important concepts to ensure effective functioning in a modern society. It comprises a set of continually developing methods and procedures providing the geospatial base supporting a country’s governmental, environmental, economic, and social activities. In general, the SDI framework consists of the integration of various elements including standards, policies, networks, data, and end users and application areas. The transformation of previously paper-based map data into a digital format, the emergence of GIS, and the Internet and a host of online applications (e.g., environmental impact analysis, navigation, applications of VGI data, governmental efficiency drives) have led to huge leaps forward in SDI development. However, SDI progress can be held back by numerous challenges, both technical and non-technical. The paper outlines these challenges from the perspective of the country of Iraq, where there is an absence of a clear direction towards efficient SDI operation and a lack of knowledge for establishing and managing effective SDI. These challenges could be met by considering and resolving generic issues, identified by the experiences of other nations, by researchers, and by organisations. These issues are investigated and assessed by means of a questionnaire survey and interviews, directed towards important participants in the field of SDI development in the country. The results present the SDI issues in order of relevance to assist developers and users in solving potential SDI and data integration problems within Iraq.

A building polygonal object matching method based on minimum bounding rectangle combinatorial optimization and relaxation labeling

AbstractVolunteered geographic information (VGI) is an emerging phenomenon where anyone can create geographic information and share it with others. Compared with traditional authoritative geospatial data, it has several advantages, such as enriched data, instant updates, and low cost. The object matching method is widely used in VGI quality assessment and data updates. However, VGI matching faces certain challenges, such as the levels of detail that vary from object to object, the uneven distribution of data quality, and the automated matching requirement. To resolve these problems, this article proposes a new matching method that effectively combines the advantages of minimum bounding rectangle combinatorial optimization (MBRCO) and relaxation labeling. The proposed method (1) avoids setting the similarity threshold and weights and does not require training samples. This process is realized based on contextual information and optimization. (2) It overcomes the disadvantage that the MBRCO algorithm cannot distinguish adjacent buildings with similar shapes. Our approach is experimentally validated using two publicly available spatial datasets: OpenStreetMap and AutoNavi map. The experimental studies show that the proposed automatic matching method outperforms all the threshold‐based MBRCO methods and achieves high accuracy with a precision of 97.8% and a recall of 99.2%.

ASSESSING THE RELIABILITY OF CONTRIBUTORS IN VGI USING IMPLICIT FACTORS

Abstract. VGI projects include geographic information, which are the product of many unorganized volunteers, making it a challenge to ensure the quality of their information. In this field of study, several researchers have suggested using intrinsic factors to evaluate the quality of VGI instead of using explicit methods such as comparing with real or reference datasets. In addition, the measurement of the reliability of VGI contributors as an essential intrinsic factor in determining the credibility of their contributions remains an open question. Various types of contributors’ activities and interactions are introduced and discussed in detail in this study at first. Then a comprehensive spatio-temporal contributor reliability model is proposed to assess their performance based on multiple implicit interactions between volunteers in their contribution process. Finally, several cities with different contribution rate (based on their population, number of users and area extent) are chosen and the proposed model is applied to the VGI data of selected regions, finally the results are compared and discussed.

Evaluation of GNSS-based Volunteered Geographic Information for assessing visitor spatial distribution within protected areas: A case study of the Bavarian Forest National Park, Germany

Systematic monitoring of recreational use in vulnerable ecosystems is crucial to balance human needs and site capacities. Recently, publicly available digital data, including Global Navigation Satellite System-based Volunteered Geographic Information, gained attention as a potential resource depicting visitor movement. However, there is a need to critically assess its reliability for visitor monitoring across countries, regions and available databases. Our research evaluates the usability of GNSS-based VGI-data obtained from three common platforms: GPSies, Outdooractive, and Komoot for assessing the spatial distribution of hikers in the Bavarian Forest National Park. A total sample of 1742 GNSS-tracks uploaded between 2013 and 2018 were compared across data platforms. Additionally, available systematic field counts, carried out between 2013 and 2014 (11 Eco-Counter sensors), were compared to GNSS-based VGI data uploaded within the corresponding period. The comparisons at individual and collective levels (route lengths, kernel density, optimized hotspot analysis along with fishnet-based counts of GNSS-tracks) showed similarities between VGI data platforms. Data obtained from GPSies and Outdooractive displayed a higher correlation with each other than with those obtained from Komoot. Also, for GPSies, there was a significant positive correlation between VGI-data and field count data. Data sample of Outdooractive and Komoot within the specified spatio-temporal frame was too small to compare with available field count data. We highlight the necessity of systematic validation of GNSS-based VGI data resources, being complementary rather than the primary data source in visitor monitoring and recreation planning. Also, systematic long-term visitor monitoring using other methods is crucial to assess the validity of novel data resources, such as GNSS-based VGI.

Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation

Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics.

Enhanced Surface Water Mapping: An Integrated Solution for the Humanitarian Sector

Remote sensing and geographic information technologies are widely used in various aspects of the humanitarian sector’s activities. In the surface water mapping domain, Earth Observation plays a central role, enabling action at the level of disaster response as well as in preventive risk analysis. Most methodologies, however, rely on optical image analysis techniques that are not operational in cloudy conditions, that typically occur during the rainy season. In this article, we present a methodology that exploits SAR data exclusively. Being acquired in the microwave spectrum, SAR can operate irrespective of the weather and time of day. As a use case, we demonstrate the possible intersection of surface water layers with population and VGI data, which can provide enhanced information concerning displaced people and accessibility. The study area is the administrative unit level 3 of Pibor, South Sudan, which is reported to be particularly challenging in terms of logistics as well as for its environmental conditions. The results cover a multi-temporal dataset from 2017 to 2021, and a cross-validation is provided for a selected date showing an overall accuracy of 88.74% for the relevant surface water layer.

Large Scaled Topographic Mapping and Issues in Depicting VGI and Open Data

Abstract. Open data and geospatial data collected by volunteers are nowadays easy to obtain and available with worldwide coverage through projects like OpenStreetMap. However, the use of these datasets leads to new challenges in depiction especially in the field of large scale topographic cartography. In addition to quality research, new processing and depiction methods for integrating these data are emerging. In the course of this work, specific problems of maps based on OpenStreetMap and Open Data elevation models are pointed out and possible solutions are introduced. In addition, a method for the preprocessing of contour lines is presented and the process flow is described in more detail. The goal of this work is to give insight into a toolbox of specially adapted and (semi-)automated methods. In this way, the quality standard of the depiction of topographic maps based on Open Data is to be increased, but also limitations are being shown.

RegNet: a neural network model for predicting regional desirability with VGI data

ABSTRACT Volunteered geographic information can be used to predict regional desirability. A common challenge regarding previous works is that intuitive empirical models, which are inaccurate and bring in perceptual bias, are traditionally used to predict regional desirability. This results from the fact that the hidden interactions between user online check-ins and regional desirability have not been revealed and clearly modelled yet. To solve the problem, a novel neural network model ‘RegNet’ is proposed. The user check-in history is input into a neural network encoder structure firstly for redundancy reduction and feature learning. The encoded representation is then fed into a hidden-layer structure and the regional desirability is predicted. The proposed RegNet is data-driven and can adaptively model the unknown mappings from input to output, without presumed bias and prior knowledge. We conduct experiments with real-world datasets and demonstrate RegNet outperforms state-of-the-art methods in terms of ranking quality and prediction accuracy of rating. Additionally, we also examine how the structure of encoder affects RegNet performance and suggest on choosing proper sizes of encoded representation. This work demonstrates the effectiveness of data-driven methods in modelling the hidden unknown relationships and achieving a better performance over traditional empirical methods.

VGTrust: measuring trust for volunteered geographic information

ABSTRACTVolunteered Geographic Information (VGI) has emerged as a large, up-to-date, and easily accessible data source. VGI can allow authoritative mapping agencies to undertake continuous improvement of their own data, adding a currency dimension previously unattainable due to high associated costs. VGI also benefits scientific and social research by facilitating quick and low-cost research data capture by the public. VGI, however, through its diversity of authorship, presents a quality assurance risk to the use of this data. This research presents a formulaic model that addresses VGI quality issues, by quantifying trust in VGI. Our ‘VGTrust’ model assesses information about a data author, and the spatial and temporal trust associated with the data they create, to produce an overall VGTrust rating metric. This metric is both easy to understand and interpret. A facilitated case study, ‘Building Our Footprints’ is presented which tests the feasibility of VGTrust model in a real-world data capture exercise run by Land Information New Zealand, New Zealand’s mapping organisation. By overcoming the trust issues in VGI, this research will allow the integration of VGI and authoritative data and potentially expand the application of VGI, thereby leveraging the power of the crowd for productive and innovative re-use.

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

ABSTRACTAttractive 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.

Towards Detecting Social Events by Mining Geographical Patterns with VGI Data

Detecting events using social media data is important for timely emergency response and urban monitoring. Current studies primarily use semantic-based methods, in which “bursts” of certain semantic signals are detected to identify emerging events. Nevertheless, our consideration is that a social event will not only affect semantic signals but also cause irregular human mobility patterns. By introducing depictive features, such irregular patterns can be used for event detection. Consequently, in this paper, we develop a novel, comprehensive workflow for event detection by mining the geographical patterns of VGI. This workflow first uses data geographical topic modeling to detect the hashtag communities with VGI semantic data. Both global and local indicators are then constructed by introducing spatial autocorrelation measurements. We then adopt an outlier test and generate indicator maps to spatiotemporally identify the potential social events. This workflow was implemented using a real-world dataset (104,000 geo-tagged photos) and the evaluation was conducted both qualitatively and quantitatively. A set of experiments showed that the discovered semantic communities were internally consistent and externally differentiable, and the plausibility of the detected events was demonstrated by referring to the available ground truth. This study examined the feasibility of detecting events by investigating the geographical patterns of social media data and can be applied to urban knowledge retrieval.

Risk perception during urban cycling: An assessment of crowdsourced and authoritative data

Subjective risk perception during urban cycling has been mostly investigated through questionnaire studies. However, newly available data sources promise extended possibilities for the investigation and understanding of the underlying factors. We validate the rationale for using both opportunistically available crowd-sourced data (i.e., volunteered geographic information or VGI) as well as more established but rarely investigated authoritative data as predictors of subjective cycling risk. We achieve this by correlating indicators of cycling risk extracted from both VGI and authoritative data for two different German cities with participants’ risk estimates assessed in laboratory-based virtual reality experiments. In Case 1, 15 participants (mostly undergraduate students with a mean age of 22 years old; nine of them females) were tested as a sample representing frequent and experienced cyclists, but unfamiliar with the 19 tested locations and less likely to be affected by the virtual reality setup. In Case 2, 24 new participants (mostly undergraduate students; mean age 24 years; 13 of them females) were experienced cyclists and mostly familiar with the 40 test locations located in their city of residence. For both cases, our findings provide evidence that parameters extracted from VGI (e.g., the semantic severity of the contribution and the reception by other citizens) as well as from authoritative data sources (e.g., accident statistics or Space Syntax measures) represent valid indicators for the subjectively perceived risk of cycling at a specific location. On the basis of this validation, future research can use these data sources to investigate the sources of risk perception during urban cycling in greater detail.

Data trustworthiness and user reputation as indicators of VGI quality

ABSTRACTVolunteered geographic information (VGI) has entered a phase where there are both a substantial amount of crowdsourced information available and a big interest in using it by organizations. But the issue of deciding the quality of VGI without resorting to a comparison with authoritative data remains an open challenge. This article first formulates the problem of quality assessment of VGI data. Then presents a model to measure trustworthiness of information and reputation of contributors by analyzing geometric, qualitative, and semantic aspects of edits over time. An implementation of the model is running on a small data-set for a preliminary empirical validation. The results indicate that the computed trustworthiness provides a valid approximation of VGI quality.

VGI and crowdsourced data credibility analysis using spam email detection techniques

ABSTRACTVolunteered geographic information (VGI) can be considered a subset of crowdsourced data (CSD) and its popularity has recently increased in a number of application areas. Disaster management is one of its key application areas in which the benefits of VGI and CSD are potentially very high. However, quality issues such as credibility, reliability and relevance are limiting many of the advantages of utilising CSD. Credibility issues arise as CSD come from a variety of heterogeneous sources including both professionals and untrained citizens. VGI and CSD are also highly unstructured and the quality and metadata are often undocumented. In the 2011 Australian floods, the general public and disaster management administrators used the Ushahidi Crowd-mapping platform to extensively communicate flood-related information including hazards, evacuations, emergency services, road closures and property damage. This study assessed the credibility of the Australian Broadcasting Corporation’s Ushahidi CrowdMap dataset using a Naïve Bayesian network approach based on models commonly used in spam email detection systems. The results of the study reveal that the spam email detection approach is potentially useful for CSD credibility detection with an accuracy of over 90% using a forced classification methodology.

A Kind of Improved VGI Spatial Association Rule Mining Algorithm Based on Multi-level Semantic Constraints

VGI information is a kind of multi-source data absolutely, which contains spatial data and tagging data. VGI data have multi-level semantic. Spatial data mining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging from remote sensing to geographical information systems, VGI data, computer cartography, environmental assessment and planning. The paper proposes a kind of VGI spatial association rule mining, which can extract frequent set quickly from VGI data. The experiment shows that the proposed algorithm is valid and efficient.

DOES COIMBRA NEED A 3D CADASTRE? PROTOTYPING A CROWDSOURCING APP AS A FIRST STEP TO FINDING OUT

Abstract. The Municipality of Coimbra in Portugal, and indeed the country as a whole, is currently undergoing a long-term land registration (cadastre creation) exercise, with approximately 50 % of the country having been surveyed, amounting to 1/3 of the total properties, by the end of 2013. The survey process is currently generating two-dimensional (2D) maps. However, as with many other countries, these maps have limitations when representing the real three-dimensional (3D) complexities of land and property ownership. Capturing 2D cadastre is an expensive process, and does not provide the required insight into the number of properties where the ownership situation is inadequately represented, as the survey does not include the internal building structure. Having information about the extent of the 2D/3D issue is, however, fundamental to making a decision as to whether to invest resources in even more expensive 3D survey. Given that the 3D complexity inside buildings is only known to residents/occupants - thus making crowd sourcing perhaps the only economically feasible approach for its capture - this paper describes the development of a web-based App envisaged for use by the general public to flag different land and property ownership situations. The paper focuses on two aspects of the problem - firstly, identifying an appropriate, clear, set of diagrams depicting the various different ownership situations from which the user can then pick one, and secondly prototyping and user testing an App for multi-platform VGI data capture in absence of direct feedback from the final end users - i.e. the general public.

Implementing a real-time Twitter-based system for resource dispatch in disaster management

This study investigates the possibility of utilizing VGI in disaster management. Effective cross-jurisdictional disaster management requires real-time information, which is not available from official sources. This paper identifies tweets from Twitter as a potential VGI data source and shows how to discover and utilize relevant tweets. The paper proposes research methods for real-time (or near real-time) tweets harvesting, live tweets saving in a distributed geodatabase, and real-time VGI data redistribution. The study implements a Web GIS application as a platform for geo-tagged tweets operation. The implemented Web GIS application includes a tweet discovery component, a geo-tagged tweets mapping component, as well as an online geo-tagged tweets operation and analysis component. The major tasks include how to record the harvested geo-tagged tweets in a geodatabase so that it can be redistributed in real-time. Based on tweets from Hurricane Joaquin in 2015 and a hypothetical mass evacuation, the case study evaluates the pros and cons of VGI for response in emergency management. Spatial–temporal analysis components are also demonstrated.

Analysis of behaviour of vehicles using VGI data

ABSTRACTThis article describes a methodology for analysing the behaviour of vehicles on roads using data obtained from Volunteered Geographic Information and more specifically from GNSS traces. These data have a great potential for this goal due to their distribution, continuity and anonymity. The proposed methodology includes all possible stages, from traces selection and downloading, passing by filtering, matching and enrichment using official linestrings, to the final obtaining of results. The article also shows the main results obtained after applying this methodology to a real case that uses a large quantity of traces, distributed over a large zone of study, including several types of roads and conditions. These results allow us to analyse the behaviour of the implicated vehicles based on the speed and the acceleration or deceleration of each trackpoint which composes the traces and compare them with the general official data published by the traffic authorities. The analysis of the results has demonstrated the viability of this methodology and its possible implementation by traffic authorities in order to obtain information to improve traffic safety.

Curating Transient Population in Urban Dynamics System

GIScience 2016 Short Paper Proceedings Curating Transient Population in Urban Dynamics System Gautam S. Thakur, Kevin A. Sparks, Robert N. Stewart, Marie L. Urban, Budhendra L. Bhaduri The Geographic Information Science and Technology (GIST) Group Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831 USA Email: {thakurg, sparksa, stewartrn, urbanml, bhaduribl}@ornl.gov Abstract Research efforts in population modelling has proven its efficacy in understanding the basic information about residential and commercial areas, as well as for the purposes of planning, development and improvement of the community as an eco-system. Limited by our current ability to capture the dynamics of population change at a finer resolution of space and time, more or less, such efforts assume static nature of population. Today, more and more people are becoming mobile, traveling across borders impacting the nuts and bolts of our urban fabric. Unfortunately, our current efforts are being surpassed by the need to capture such transient population. It is becoming imperative to identify and define them, as well as measure their dynamics and interconnectedness. In this work, we intend to research urban population mobility patterns, gauge their transient nature, and extend our knowledge of their visited locations. We plan to achieve this by designing and developing novel methods and using VGI data that models and characterizes transient population dynamics. 1. Introduction Over the last decade, worldwide percentages of transient population i.e. population on-the- move have grown linearly, on account of socio-economic development and rising global conflicts (Worldbank 2016). Characterizing such movement patterns are vital to understand the dynamics of an urban system. Beyond that, privacy-preserved and discrete insight into the diurnal activities of people, where they are, where they go, and how much time they spent at certain locations is vital for first responders in times of emergencies, and for urban planners to develop robust future cities. Traditionally, approaches involved in curating such information have heavily relied on census data, surveys, and simulations models. While they provide an excellent source of baseline statistics - for logistical purposes, they rarely collect the spatio-temporal distribution relating to the dynamics of population movements. Furthermore, they omit non-residential locations, such as business districts, parks, and museums. Social media(Hawelka et al. 2014; Frias-Martinez et al. 2012), cellular data(Di Lorenzo & Calabrese 2011; Calabrese et al. 2010; Calabrese et al. 2011; Calabrese et al. 2013; Isaacman et al. 2012) have proved their efficacies in representing global patterns of human mobility. However, much of the current research has not focused on curating population distribution on a temporal scale (e.g. Census data focuses on residential or night time population). This work attempts to provide a set of guidelines that help infer the change in population numbers at different temporal scales. In this work, first we propose Transient Population Dynamics Model that attempts to describe and model the mobility patterns of active population. In addition, it provides approaches to identify and estimate transient population and aid in discovering new locations and the duration of their visits. Second, we utilize this model in estimating the transient population across Australia and compare the differences against respective census data. Also, we intend to discover frequently visited locations in outback and metropolitan areas. We believe this work will generate enthusiasm among researchers and make a case for the importance of curating transient population.

Preučevanje popolnosti podatkov na podlagi primerjalne analize med podatki VGI in uradnimi podatkovnimi nizi o stavbah

Preučevanje popolnosti podatkov na podlagi primerjalne analize med podatki VGI in uradnimi podatkovnimi nizi o stavbah