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.     

Quality assessment for building footprints data on OpenStreetMap

In the past two years, several applications of generating three-dimensional (3D) buildings from OpenStreetMap (OSM) have been made available, for instance, OSM-3D, OSM2World, OSM Building, etc. In these projects, 3D buildings are reconstructed using the buildings’ footprints and information about their attributes, which are documented as tags in OSM. Therefore, the quality of 3D buildings relies strongly on the quality of the building footprints data in OSM. This article is dedicated to a quality assessment of building footprints data in OSM for the German city of Munich, which is one of the most developed cities in OSM. The data are evaluated in terms of completeness, semantic accuracy, position accuracy, and shape accuracy by using building footprints in ATKIS (German Authority Topographic–Cartographic Information System) as reference data. The process contains three steps: finding correspondence between OSM and ATKIS data, calculating parameters of the four quality criteria, and statistical analysis. The results show that OSM footprint data in Munich have a high completeness and semantic accuracy. There is an offset of about four meters on average in terms of position accuracy. With respect to shape, OSM building footprints have a high similarity to those in ATKIS data. However, some architectural details are missing; hence, the OSM footprints can be regarded as a simplified version of those in ATKIS data.     

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     

Travel impedance agreement among online road network data providers

ABSTRACT

Online mapping providers offer unprecedented access to spatial data and analytical tools; however, the number of analytical queries that can be requested is usually limited. As such, Volunteered Geographic Information (VGI) services offer a viable alternative, provided that the quality of the underlying spatialtheir data is adequate. In this paper, we evaluate the agreement in travel impedance between estimates from MapQuest Open, which embraces OpenStreetMap (OSM) data–a is based on VGI datasetfrom OpenStreetMap (OSM), and estimates from two other popular commercial providers, namely Google Maps? and ArcGIS? Online. Our framework is articulated around three components, which simulates potentialcalculates shortest routes, estimates their travel impedance using a routing service Application Program Interface (API), and extracts the average number of contributors for each route. We develop an experimental setup with a simulated dataset for the state of North Carolina. Our results suggest a strong correlation of travel impedance among all three road network providers. and that travel impedanceThe agreement is the greatest in areas with a denser road network and the smallest for routes of shorter distances. Most importantly, tTravel estimates from MapQuest Open are nearly identical to both commercial providers when the average number of OSM contributors along the route is larger. The latter finding contributes to a growing body of literature on Linus’s law, recognizing that a larger group of contributors holds the potential to validate and correct inherent errors to the source dataset.     

Geometric-based approach for integrating VGI POIs and road networks

Integrating heterogeneous spatial data is a crucial problem for geographical information systems (GIS) applications. Previous studies mainly focus on the matching of heterogeneous road networks or heterogeneous polygonal data sets. Few literatures attempt to approach the problem of integrating the point of interest (POI) from volunteered geographic information (VGI) and professional road networks from official mapping agencies. Hence, the article proposes an approach for integrating VGI POIs and professional road networks. The proposed method first generates a POI connectivity graph by mining the linear cluster patterns from POIs. Secondly, the matching nodes between the POI connectivity graph and the associated road network are fulfilled by probabilistic relaxation and refined by a vector median filtering (VMF). Finally, POIs are aligned to the road network by an affine transformation according to the matching nodes. Experiments demonstrate that the proposed method integrates both the POIs from VGI and the POIs from official mapping agencies with the associated road networks effectively and validly, providing a promising solution for enriching professional road networks by integrating VGI POIs.     

Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns

In the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distance-based coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects.     

OpenStreetMap and Food Security: A Case Study in the City of Philadelphia

OpenStreetMap (OSM) is an online public access database that allows for the collaborative collection of local geographic information. We employ this mapping technology to discuss a new social theory of poverty that moves away from income poverty to an economy that directly produces individuals' basic needs. Focusing on urban farming in Philadelphia as an example, we use OSM to support the argument that money, land, labor, and capital do not limit food production in the city. OSM is a type of “commons” that allows community members to depict features of interest to them that might otherwise be underrepresented in official or commercially produced maps such as Google Maps. Using the concept of facilitated volunteered geographic information (VGI) we developed an open framework for combining residents' local knowledge of food resources with expert guidance in data input. We believe this helps overcome problems with ad hoc data submission efforts to which collaborative online projects are susceptible. The program for “tagging” food resources in OSM was deployed in a public “map-a-thon” event we organized in Philadelphia, bringing together technical experts and food enthusiasts. To share the results, we present the Philly Fresh Food Map as an interactive online Web map that can be used and updated by the public.     

A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena

Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy.     

An evaluation of crowdsourced information for assessing the visitation and perceived importance of protected areas

Parks and protected areas provide a wide range of benefits, but methods to evaluate their importance to society are often ad hoc and limited. In this study, the quality of crowdsourced information from Public Participation GIS (PPGIS) and Volunteered Geographic Information (VGI) sources (Flickr, OpenStreetMap (OSM), and Wikipedia) was compared with visitor counts that are presumed to reflect social importance. Using the state of Victoria, Australia as a case study, secondary crowdsourced VGI data, primary crowdsourced (PPGIS data) and visitor statistics were examined for their correspondence and differences, and to identify spatial patterns in park popularity. Data completeness—the percent of protected areas with data—varied between sources, being highest for OSM (90%), followed by Flickr (41%), PPGIS (24%), visitation counts (5%), and Wikipedia articles (4%). Statistically significant correlations were found between all five measures of popularity for protected areas. Using stepwise multiple linear regression, the explained variability in visitor numbers was greater than 70%, with PPGIS, Flickr and OSM having the largest standardized coefficients. The social importance of protected areas varied as a function of accessibility and the types of values (direct or indirect use) expressed for the areas. Crowdsourced data may provide an alternative to visitor counts for assessing protected area social importance and spatial variability of visitation. However, crowdsourced data appears to be an unreliable proxy for the full range of values and importance of protected areas, especially for non-use values such as biological conservation.     

Assessing similarity matching for possible integration of feature classifications of geospatial data from official and informal sources

One difficulty in integrating geospatial data sets from different sources is variation in feature classification and semantic content of the data. One step towards achieving beneficial semantic interoperability is to assess the semantic similarity among objects that are categorised within data sets. This article focuses on measuring semantic and structural similarities between categories of formal data, such as Ordnance Survey (OS) cartographic data, and volunteered geographic information (VGI), such as that sourced from OpenStreetMap (OSM), with the intention of assessing possible integration. The model involves ‘tokenisation’ to search for common roots of words, and the feature classifications have been modelled as an XML schema labelled rooted tree for hierarchical analysis. The semantic similarity was measured using the WordNet::Similarity package, while the structural similarities between sub-trees of the source and target schemas have also been considered. Along with dictionary and structural matching, the data type of the category itself is a comparison variable. The overall similarity is based on a weighted combination of these three measures. The results reveal that the use of a generic similarity matching system leads to poor agreement between the semantics of OS and OSM data sets. It is concluded that a more rigorous peer-to-peer assessment of VGI data, increasing numbers and transparency of contributors, the initiation of more programs of quality testing and the development of more directed ontologies can improve spatial data integration.     

Volunteered geographic information quality assessment using trust and reputation modelling in land administration systems in developing countries

This article presents an innovative approach to establish the quality and credibility of Volunteered Geographic Information (VGI) such that it can be considered in Land Administration Systems (LAS) on a Fit for Purpose (FFP) basis. A participatory land information system can provide affordable and timely FFP information about land and its resources. However, the establishment of such a system involves more than just technical solutions and administrative procedures: many social, economic and political aspects must be considered. Innovative approaches like VGI can help address the lack of accurate, reliable and FFP land information for LAS, but integration of such sources relies on the quality and credibility of VGI. Verifying volunteer efforts can be difficult without reference to ground truth: a novel Trust and Reputation Modelling methodology is proposed as a suitable technique to effect such VGI data set validation. This method has been applied to successfully demonstrate that VGI can produce accurate and reliable data sets which can be used to conduct regular systematic updates of geographic information in official systems. It relies on a view that the public can police themselves in establishing proxy measures of VGI quality thus facilitating VGI to be used on a FFP basis in LAS.     

Analysis of positional uncertainty of road networks in volunteered geographic information with a statistically defined buffer-zone method

Volunteered geographic information (VGI) is crowdsourced information that can enrich and enhance research and applications based on geo-referenced data. However, the quality of VGI is of great concern, and positional accuracy is a fundamental basis for the VGI quality assurance. A buffer-zone method can be used for its assessment, but the buffer radius in this technique is subjectively specified; as result, different selections of the buffer radius lead to different positional accuracies. To solve this problem, a statistically defined buffer zone for the positional accuracy assessment in VGI is proposed in this study. To facilitate practical applications, we have also developed an iterative method to obtain a theoretically defined buffer zone. In addition to the positional accuracy assessment, we have derived a measure of positional quality, which comprises the assessment of positional accuracy and the level of confidence in such assessment determined with respect to a statistically defined buffer zone. To illustrate and substantiate the theoretical arguments, both numerical simulations and real-life experiments are performed using OpenStreetMap. The experimental results confirm the high significance of the proposed statistical approach to the buffer zone-based assessment of the positional uncertainty in VGI.     

Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data

Fine-scale population distribution data at the building level play an essential role in numerous fields, for example urban planning and disaster prevention. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited numerous population distribution mapping studies. However, most of these studies focused on global population and environmental changes; few considered fine-scale population mapping at the local scale, largely because of a lack of reliable data and models. As geospatial big data booms, Internet-collected volunteered geographic information (VGI) can now be used to solve this problem. This article establishes a novel framework to map urban population distributions at the building scale by integrating multisource geospatial big data, which is essential for the fine-scale mapping of population distributions. First, Baidu points-of-interest (POIs) and real-time Tencent user densities (RTUD) are analyzed by using a random forest algorithm to down-scale the street-level population distribution to the grid level. Then, we design an effective iterative building-population gravity model to map population distributions at the building level. Meanwhile, we introduce a densely inhabited index (DII), generated by the proposed gravity model, which can be used to estimate the degree of residential crowding. According to a comparison with official community-level census data and the results of previous population mapping methods, our method exhibits the best accuracy (Pearson R = .8615, RMSE = 663.3250, p < .0001). The produced fine-scale population map can offer a more thorough understanding of inner city population distributions, which can thus help policy makers optimize the allocation of resources.     

Exploring the relationship between density and completeness of urban building data in OpenStreetMap for quality estimation

OpenStreetMap (OSM) is a free spatial data source based on crowd sourced data. Although the OSM data have a range of applications, such as generating 3D models, and routing and navigation, quality issues are still significant concerns when using the data. Several studies have undertaken quality assessments by comparing OSM data with reference data. However, reference data are not always available due to high costs or licensing restrictions, and very few studies have quantitatively estimated the quality of OSM data under conditions where the corresponding reference data are not available. This study proposed the use of a building density (or building coverage ratio) indicator as a proxy, and designed a series of experiments involving different study areas to quantitatively explore the relationship between building density and building completeness for OSM data in urban areas. The residuals (estimated building completeness and reference building completeness) were also analyzed. Two main results were found from the experiments. (1) There was an approximate linear relationship between building density and building completeness in the OSM data. More precisely, the building completeness of OSM data was approximately 3.4–4 times the building density of OSM data. (2) Approximately 70–80% of the absolute residuals were smaller than 10%, and 80–90% of them were smaller than 20%. This shows that, in most cases, estimated building completeness was close to the corresponding reference building completeness. Therefore, we concluded that the building density indicator is a potential proxy for the quantitative completeness estimation of OSM building data in urban areas. The limitations of using this indicator were also addressed.     

Toward mapping land-use patterns from volunteered geographic information

A large number of applications have been launched to gather geo-located information from the public. This article introduces an approach toward generating land-use patterns from volunteered geographic information (VGI) without applying remote-sensing techniques and/or engaging official data. Hence, collaboratively collected OpenStreetMap (OSM) data sets are employed to map land-use patterns in Vienna, Austria. Initially the spatial pattern of the landscape was delineated and thereafter the most relevant land type was assigned to each land parcel through a hierarchical GIS-based decision tree approach. To evaluate the proposed approach, the results are compared with the Global Monitoring for Environment and Security Urban Atlas (GMESUA) data. The results are compared in two ways: first, the texture of the resulting land-use patterns is analyzed using texture-variability analysis. Second, the attributes assigned to each land segment are evaluated. The achieved land-use map shows kappa indices of 91, 79, and 76% agreement for location in comparison with the GMESUA data set at three levels of classification. Furthermore, the attributes of the two data sets match at 81, 67, and 65%. The results demonstrate that this approach opens a promising avenue to integrate freely available VGI to map land-use patterns for environmental planning purposes.     

自发地理信息在灾后恢复监测中的应用研究框架

文章探讨了如何有效利用自发地理信息（Volunteered Geographic Information, VGI）大数据促进灾后恢复监测工作。首先概述了国内外VGI相关研究的发展现状,明确了VGI用于灾后恢复监测研究的不足,然后提出了一个基于VGI大数据的灾后恢复监测应用的研究框架,助力于灾后恢复监测各类具体恢复目标（如旅游业恢复、工商业恢复、生活常态恢复）的实现。该研究框架包含数据获取、数据质量控制和数据挖掘3个核心组成部分。其中,数据获取对象以VGI为主,以传统官方权威数据为辅;数据质量控制主要是通过模糊逻辑专家系统和人工神经网络（深度学习）确保VGI适用性;数据挖掘则是以变革式范例为理论基础,利用定量和定性结合的方法调查灾区基建、经济和安全3个灾后恢复主要方面的状态。最后,文章还讨论了当前利用VGI大数据促进灾后恢复监测所存在的一些局限性,包括VGI来源的可持续性问题、各VGI平台应用程序接口的数据获取限制问题和VGI应用所涉及的用户隐私问题。     

Assessing the applicability of OpenStreetMap data to assist the validation of land use/land cover maps

The validation of land use/land cover (LULC) maps is usually performed using a reference database consisting of a sample of points or regions to which the ‘real’ class is assigned. This assignment is usually performed by specialists using photointerpretation (PI) of high-resolution imagery and/or field visits, which are time consuming and expensive processes. The aim of this article is to assess if the data available in the collaborative project OpenStreetMap (OSM) may be used as a source of data to assist the creation of these reference databases, reducing the time spent and costs associated with their generation. For this aim, two case studies were used, where the validation of the Global Monitoring for Environment and Security Urban Atlas (UA) was performed. The used methodology requires the harmonization of the data available in OSM with the UA nomenclature, and the subsequent creation of a LULC map from the OSM data. This map was then compared to UA to assess the similarity of the regions mapped in both. To test the usefulness of OSM data to assess the accuracy of UA, a sample of points was created and two reference databases generated, one assigning the data extracted automatically from OSM to the points where these data were available, and PI for the remaining points, and the other using only PI. The accuracy assessment of UA for the two case studies was then made building confusion matrixes and computing accuracy indicators. The results showed that for the two study areas, only low percentages of points had to be photo interpreted in the first reference database (respectively, 12% and 2% for the two study areas), decreasing the work load considerably. The results obtained with both reference databases are comparable for level 1 classes. For level 2 classes, worse results were obtained for some classes, showing that the OSM data used are not enough to create reliable reference data.     

Utilising volunteered geographic information to assess resident's flood evacuation shelters. Case study: Jakarta

Research on disaster response frequently uses volunteered geographic information (VGI), due to its capability to provide near real-time information during and after a disaster. It is much less commonly used in spatial planning related to disaster management. However, VGI appears to have considerable potential for use in spatial planning and offers some advantages over traditional methods. For example, VGI can capture residents' preferences in a much faster, more timely, and more comprehensive fashion than is possible with, for example, questionnaires and surveys. This research investigates the usefulness of VGI for planning flood evacuation shelters. Using Jakarta, Indonesia, as a case study, we use VGI to capture the locations of flood evacuation shelters based on residents' preferences during flood periods in 2013–2014 and 2014–2015 and compare these with the locations of official shelters. Floods frequently affect Jakarta and the city administration uses VGI in flood emergency responses. Moreover, Jakarta has been identified as having the largest number of active Twitter users among cities worldwide. Thus, Jakarta is an appropriate place to study the use of VGI for planning evacuation shelters. VGI generated by Twitter users was used to identify the shelter locations preferred by Jakarta residents, and more precisely the flood evacuees. Of 171,046 tweets using keywords relating to flood evacuation, the content of 306 tweets indicated that they had been sent from inside or near evacuation shelters. The spatial pattern showed that those tweets were sent from 215 locations, mostly located near flooded areas. The analysis further showed that 35.6% of these shelter locations preferred by residents intersected with the locations of official evacuation shelters. As a general conclusion, our study demonstrates the advantages of using VGI for spatial planning, which mainly relates to the ease of capturing community preferences over a large area.     

Coverage inequality and quality of volunteered geographic features in Chinese cities: Analyzing the associated local characteristics using geographically weighted regression

The volunteered geographic information (VGI) gains increasing popularity with the general public and scientific community. However, the optimism about the VGI has been tempered by two critical issues: inequality in data coverage (social justice) and data quality. It therefore requires a better understanding of the mechanism driving VGI contributions and content quality. With a case of China, this paper demonstrates one potential avenue, examining the associations between VGI coverage/quality and local demographic and socioeconomic characteristics. In particular, VGI data are harvested from the OpenStreetMap for 333 cities in China. VGI coverage is measured by the total volume of different geographic features (point, line and polygon); and VGI quality is described from two aspects: completeness and accuracy. Geographically weighted regression (GWR) shows that both demographic and socioeconomic factors have statistically significant influences on VGI coverage and quality. More specifically, densely populous cities with more young, educated and non-agricultural people enjoy higher VGI coverage and quality. Cities with lower VGI coverage and quality are primarily located in the western and southwestern regions where the ethnic minorities concentrate. High VGI coverage and quality are possibly observed in economically developed cities with high marketization degree. Besides, possibility of high VGI coverage and quality occurs in cities with more labor in scientific research and greater percentage of employers in the tertiary industry. The GWR also demonstrates that the strength and nature of the obtained relationships vary across the 333 cities. The spatial non-stationary relationships may partially answer for the controversial empirical conclusions in earlier case studies at different scales. Quantitative analysis (Gini index, Lorenz curve and Moran's I index) further evidences the great inequality in VGI coverage and quality. It can be safely inferred that the differences in engagement and use of VGI, as a new digital divide, can raise troubling concerns on the social justice implications.     

The recent advancement in digital terrain analysis and modeling

Volunteered Geographic Information (VGI) represents a growing source of potentially valuable data for many applications, including land cover map validation. It is still an emerging field and many different approaches can be used to take value from VGI, but also many pros and cons are related to its use. Therefore, since it is timely to get an overview of the subject, the aim of this article is to review the use of VGI as reference data for land cover map validation. The main platforms and types of VGI that are used and that are potentially useful are analysed. Since quality is a fundamental issue in map validation, the quality procedures used by the platforms that collect VGI to increase and control data quality are reviewed and a framework for addressing VGI quality assessment is proposed. A review of cases where VGI was used as an additional data source to assist in map validation is made, as well as cases where only VGI was used, indicating the procedures used to assess VGI quality and fitness for use. A discussion and some conclusions are drawn on best practices, future potential and the challenges of the use of VGI for land cover map validation.