Volunteered Geographic Information Research Articles

Proposal for an index of roads and structures for the mapping of non-vegetated urban surfaces using OSM and Sentinel-2 data

The use of volunteered geographic information (VGI), such as OpenStreetMap (OSM), to assist in mapping land use and coverage together with remote sensing images is relatively recent. Most studies have used OSM to assist in sample collection for image classification or aggregated vectors of buildings, transport, and land-use and coverage as ancillary data to support mapping refinements. This study proposes a metric called the “index of roads and structures” (IRS) created on the basis of OSM data with the intention of assisting in the mapping of non-vegetated and non-aquatic urban surfaces. IRS thresholds were defined and supplemented with information derived from the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) as a way of extending the restriction between urban and non-urban classes and thus achieving better mapping accuracy. To implement this study, multispectral Sentinel-2 images resampled to 10 m on the ground were processed in the Google Earth Engine (GEE). The IRS is a raster file, in which each pixel is associated with the possibility of being inserted in an urban context; thus, the importance of this index as an aid in mapping urban areas is clear. We have demonstrated the possibility of using the IRS to map non-vegetated urban surfaces in Brazil (8.5 million square kilometers) and obtained a very high accuracy of 91.2%.

Uncertainties in Spatial Orientation: Critical Limits for Landmark Inaccuracies in Maps in the Context of Map Matching

Map information, especially volunteered geographic information (VGI) is prone to spatial inaccuracies. Due to their use as spatial reference points, spatially inaccurate landmark representations in maps might affect the ability to match maps to the represented 3D space and might compromise self-localization and orientation. Based on a map matching task in a virtual 3D environment and various degrees of spatial landmark inaccuracies in a simultaneously presented 2D map, we aimed to identify acceptable and critical values of spatial inaccuracies. Furthermore, potential effects of inaccurate semantic spatial categories were evaluated. The findings demonstrate how metric and semantic spatial inaccuracies of landmark representations affect matching of maps to the represented 3D space. Map inaccuracies corresponding to more than 10 m within 3D space and swapped landmark pictograms in a map were associated with the perception of a mismatch between maps and 3D spaces. Furthermore, the distance of landmarks to the perceiver was found to affect map matching. Spatial inaccuracies of landmark pictograms were less likely to be associated with a perceived mismatch between maps and 3D spaces when the landmarks were further away, presumably because small or moderate inaccuracies were more difficult to perceive. To reduce the identified effects of landmark inaccuracies in maps on map-matching performance, we propose to identify means to quantify the uncertainty concerning spatial landmark inaccuracies and to visually communicate this uncertainty to map users.

Centaur VGI: An Evaluation of Engagement, Speed, and Quality in Hybrid Humanitarian Mapping

Volunteered geographic information (VGI) is often cited as a potential solution to persistent global inequalities in map data, particularly in areas undergoing humanitarian crises. Poor volunteer engagement, slow data production, and low-quality outputs have limited progress, however, and can unintentionally exaggerate inequalities. Hybrid machine learning–VGI (ML–VGI) frameworks can help to overcome these challenges through a combination of workflow automation and purposive human input, but the use of these workflows is rare in practice. Here, we implement an ML–VGI framework (Centaur VGI) and undertake a detailed comparative usability assessment against an existing, widely used VGI mapping platform to demonstrate its potential to improve volunteer engagement, mapping speed, and data quality. Our results suggest that through automated building, searching, and labeling, the Centaur VGI platform provides greater usability, quicker data production, and improved data quality for most users. Consequently, we provide the first evidence that hybrid ML–VGI approaches can be used to facilitate increased public participation in humanitarian building mapping efforts and thus help reduce global inequalities in map data.

Evaluating the usefulness of VGI from Waze for the reporting of flash floods

Using volunteered geographic information (VGI) to supplement disaster risk management systems, including forecasting, risk assessment, and disaster recovery, is increasingly popular. This attention is driven by difficulties in detection and characterization of hazards, as well as the rise of VGI appropriate for characterizing specific forms of risk. Flash-flood historical records, especially those that are impact-based, are not comprehensive, leading to additional barriers for flash-flood research and applications. In this paper we develop a method for associating VGI flood reporting clusters against authoritative data. Using Hurricane Harvey as a case study, VGI reports are assimilated into a spatial analytic framework that derives spatial and temporal clustering parameters supported by associations between Waze’s community-driven emergency operations center and authoritative reports. These parameters are then applied to find previously unreported likely flash flood-events. This study improves the understanding of the distribution of flash flooding during Hurricane Harvey and shows potential application to events in other areas where Waze data and reporting from official sources, such as the National Weather Service, are available.

Integration of GNSS observations with volunteered geographic information for improved navigation performance

Abstract Pedestrian and vehicular navigation relies mainly on Global Navigation Satellite System (GNSS). Even if different navigation systems are integrated, GNSS positioning remains the core of any navigation process as it is the only system capable of providing independent solutions. However, in harsh environments, especially urban ones, GNSS signals are confronted by many obstructions causing the satellite signals to reach the receivers through reflected paths. These No-Line of Sight (NLOS) signals can affect the positioning accuracy significantly. This contribution proposes a new algorithm to detect and exclude these NLOS signals using 3D building models constructed from Volunteered Geographic Information (VGI). OpenStreetMap (OSM) and Google Earth (GE) data are combined to build the 3D models incorporated with GNSS signals in the algorithm. Real field data are used for testing and validation of the presented algorithm and strategy. The accuracy improvement, after exclusion of the NLOS signals, is evaluated employing phase-smoothed code observations. The results show that applying the proposed algorithm can improve the horizontal positioning accuracy remarkably. This improvement reaches 10.72 m, and the Root Mean Square Error (RMSE) drops by 1.64 m (46 % improvement) throughout the epochs with detected NLOS satellites. In addition, the improvement is analyzed in the Along-Track (AT) and Cross-Track (CT) directions. It reaches 6.89 m in the AT direction with a drop of 1.076 m in the RMSE value, while it reaches 8.64 m with a drop of 1.239 m in the RMSE value in the CT direction.

Compound flood modeling framework for surface–subsurface water interactions

Abstract. Compound floods are an active area of research in which the complex interaction between pluvial, fluvial, coastal and groundwater flooding are analyzed. A number of studies have simulated the compound flooding impacts of precipitation, river discharge and storm surge variables with different numerical models and linking techniques. However, groundwater flooding is often neglected in flood risk assessments due to its sporadic frequency (as most regions have water tables sufficiently low that do not exacerbate flooding conditions), isolated impacts and considerably lower severity with respect to other types of flooding. This paper presents a physics-based, loosely coupled modeling framework using FLO-2D and MODFLOW-2005 that is capable of simulating surface–subsurface water interactions. FLO-2D, responsible for the surface hydrology and infiltration processes, transfers the infiltration volume as recharge to MODFLOW-2005 until the soil absorption capacity is exceeded, while MODFLOW-2005 returns exchange flow to the surface when the groundwater heads are higher than the surface depth. Three events characterized by short-duration intense precipitation, average tide levels and unusually high water table levels are used to assess the relevance of groundwater flooding in the Arch Creek Basin, a locality in North Miami particularly prone to flooding conditions. Due to limitations in water level observations, the model was calibrated based on properties that have experienced repetitive flooding losses and validated using image-based volunteer geographic information (VGI). Results suggest that groundwater-induced flooding is localized, and high groundwater heads influence pluvial flooding as the shallow water table undermines the soil infiltration capacity. Understanding groundwater flood risk is of particular interest to low-elevation coastal karst environments as the sudden emergence of the water table at ground surface can result in social disruption, adverse effects to essential services and damage to infrastructure. Further research should assess the exacerbated impacts of high tides and sea level rise on water tables under current and future climate projections.

Inference of positional accuracy of collaborative data from intrinsic parameters

AbstractUpdating reference maps is the focus of research on the use of voluntary geographic information (VGI) in countries with spatial data deficits. However, data quality needs to be known so collaborative data can fill the geospatial data gaps in this situation. Most research uses comparative metrics for this purpose (i.e. they compare collaborative data with official data considered more reliable). However, this study proposes a method for cases with uneven coverage of available reference data. Therefore, we propose to use the geographically weighted regression method with the definition of collective spatial intelligence as a parameter to explain the positional accuracy of collaborative data. The number of collaborators and edits observed on OpenStreetMap features was used to represent this parameter. The results show that such variables are significant and can explain up to 93% of the positional accuracy, which points the way to estimate the positional accuracy of VGI without direct comparisons between collaborative data and data from official sources.

Corporate editors in OpenStreetMap: Investigating co‐editing patterns

AbstractTraditionally, OpenStreetMap (OSM) has been recognized as a volunteered geographic information (VGI) project. In recent years, many corporations have enlisted teams of mappers to edit data on OSM. These teams of corporate editors (CEs) can quickly edit large swaths of data using a variety of methods. Consequently, there are new tensions over possible community bifurcations where editing and map stewardship disagreements may occur between the CEs and non‐CEs. To characterize CE and non‐CE editing interactions, we focused on six locations with varied types of corporate editing activity. We created six temporal (2015–2020) editing networks for each location, resulting in 36 total networks. We found a continual increase in the number of editors, with more growth in places with CEs. There was significant co‐editing between the two groups, with CEs showing more in‐group editing patterns, both in terms of number of edits and time between edits. We conclude that currently the CE and the non‐CE communities continue to co‐exist and co‐produce open geospatial data in apparent harmony, even though the size of the CE community and volume of contributions have grown significantly. Finally, we discuss implications for OSM as a VGI project in light of our corporate editing trends.

RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments

AbstractInternet of Things (IoT) has a pivotal role in developing intelligent and computational solutions to facilitate varied real‐life applications. To execute high‐end computations and data analytics, IoT and cloud‐based solutions play the most significant role. However, frequent communication with long distant cloud servers is not a delay‐aware and energy‐efficient solution while providing time‐critical applications such as healthcare. This article explores the possibilities and opportunities of integrating cloud technology with fog and edge‐based computing to provide healthcare services to users in exigency. Here, we propose an end‐to‐end framework namedRESCUE(enabling green healthcare services using integrated iot‐edge‐fog‐cloud computing environments), consisting efficient spatio‐temporal data analytics module for efficient information sharing, spatio‐temporal data analysis to predict the path for users to reach the destination (healthcare center or relief camps) with minimum delay in the time of exigency (say, natural disaster). This module analyzes the collected information through crowd‐sourcing and assists the user by extracting optimal path postdisaster when many regions are nonreachable. Our work is different from the existing literature in varied aspects: it analyses the context and semantics by augmenting real‐time volunteered geographical information (VGI) and refines it. Furthermore, the novel path prediction module incorporates such VGI instances and predicts routes in emergencies avoiding all possible risks. Also, the design of development of a latency‐aware, power‐aware data‐driven analytics system helps to resolve any spatio‐temporal query more efficiently compared to the existing works for any time‐critical application. The experimental and simulation results outperform the baselines in terms of accuracy, delay, and power consumption.

Volunteered Geographical Information and Recreational Uses within Metropolitan and Rural Contexts

Data obtained through Volunteered Geographical Information (VGI) have gradually been used to monitor and support planning mainly in urban contexts. Regarding recreational activities in peri-urban green and natural areas, VGI has been used to map, measure use intensity, profile users, and evaluate their preferences and motivations. Given their extensive use, it is now worthwhile to assess the value of VGI data to (1) compare recreational uses, profile users and map recreational activities in different contexts (metropolitan vs. rural areas), and (2) evaluate outdoor and adventure tourist products such as Grand Routes (GR). Data from former GPSies (AllTrails nowadays), one of the most popular web-share services, were used to assess recreational uses in Lisbon Metropolitan Area (LMA) and southwest Portugal (SWPT). A set of 22,031 tracks of “on foot” and “on wheels” activities, submitted by 3297 national and foreign users, covering 12 years, was analysed within a GIS modelling environment. Results indicate that, although there are many more submissions in the LMA, the influence of foreigners in the SWPT is higher (11% vs. 19%). The existing GR in SWPT concentrates the foreign use for hiking (71% of foreign vs. 28% of national users), demonstrating its attractiveness. For the favourite activity in both areas—Mountain biking—results show a higher spatial dispersion, yet part of the activity in SWPT still conforms to the GR (16% of foreign and 20% of national use). This study proves other applications for VGI, showing its usefulness for assessing recreational uses in both metropolitan and rural areas. Spatial knowledge about recreational uses is a valuable tool to evaluate and monitor such activities, and to know what users like to do, and where, and is also useful information when designing recreational products considering their tourist potential, thus adding value to these offers.

Identifying differences in roadless areas in Canada based on global, national, and regional road datasets

AbstractRoads are an overwhelming component of the global human footprint and their absence helps identify intact areas with high ecological value. Road‐free areas are decreasing globally, making accurate estimation of their location and size of great importance. Identification of such regions requires accurate data, but substantial variability exists in road network datasets created and maintained at different spatial scales. We compared estimates of road length, density, and roadless areas across Canada, which contains a high proportion of the world's remaining undisturbed and road‐free areas. Global‐ and national‐scale datasets included, on average, only 11%–14% of roads represented in regional‐scale data or volunteered geographic information (VGI), with the most pronounced differences in less‐developed areas. Regional‐scale datasets, with the lowest estimates of amount of roadless area and smallest mean roadless patch size, are likely the most complete road datasets but are not available for all jurisdictions, limiting their national‐scale utility. VGI provides a national‐scale alternative but still lacks many low‐use roads. Available global and national datasets have insufficient information for accurate assessments of roadless areas in Canada, which will require detailed, consistent subnational datasets assembled and maintained by each province and territory in a coordinated fashion to achieve national coverage.

Volunteered geographic information mobile application for participatory landslide inventory mapping

Participatory landslide inventory mapping using the Volunteered Geographic Information (VGI) mobile app is a promising method to produce a landslide inventory map. The aim of this research is to describe the development and implementation of the VGI mobile app for participatory landslide inventory mapping. The architecture VGI mobile app is developed on the basis of Free Open-source Software for Geospatial Application server-client software to ensure reproducibility and flexibility, and to reduce cost. Anyone can reproduce, modify, and share the code, which suggests improvement in the collective ability to use, prepare, and landslide inventory update. Landslide inventory using VGI mobile app shows that the tool and method successfully map landslides in the landslide prone area (Magelang Regency, Central Java Province, Indonesia) with fairly high levels of effectiveness and convenience. Magelang Regency, one of the landslide prone areas in Java, is located in the intermountain basin surrounded by Menoreh Mountain, Merapi, Merbabu, Suropati-Telomoyo Complex, and Sumbing Volcano. In this study, landslide inventory mapping using VGI mobile app was applied in Magelang Regency by 17 volunteers from BPBD (Regional Agency for Disaster Management) Magelang Regency for three days. Landslides area occurred from 2017 to 2019 were properly identified and mapped by the volunteers. The sizes of landslides varied from 5.2 m2 to 4,632.5 m2, and the average was 208.2 m2. A team of volunteer was able to map 7-10 landslides per day. Participatory mapping using VGI mobile app reduces the time in transferring field data to a GIS database, in contrast to conventional participatory landslide inventory mapping. VGI mobile app allows users to provide new geographical landslide data, share landslide data rapidly, ensure consistency of landslide data, and improve accessibility of landslide data. The use of the VGI mobile app for participatory landslide inventory mapping provides new opportunities to improve risk assessment, preparedness, and early action and warning to landslide hazard.

Analysis of Factors Affecting Adoption of Volunteered Geographic Information in the Context of National Spatial Data Infrastructure

Spatial data infrastructures (SDIs) have been implemented for the last four decades in most countries. One of the key objectives of SDIs is to ensure the quick availability and accessibility of spatial data. The success of SDI depends on the underlying spatial datasets. Many developing countries such as Pakistan are facing problems in implementing SDI because of the unavailability of spatial data. Volunteered Geographic Information (VGI) is an alternate source for obtaining spatial data. Therefore, the question is what factors hamper the adoption of VGI for making it part of SDI in Pakistan. The intention behind this paper is to explore such factors as the key research question. To do so, we make use of the Technology–Organization–Environment (TOE) framework along with the partial least square structural equation model (PLS-SEM) to empirically analyze the factors impeding VGI from becoming part of SDI in the country. The study concludes that many technical, organizational, and environmental factors affect the adoption of VGI to be part of SDI in Pakistan.

A Framework for Scaling Urban Transformative Resilience through Utilizing Volunteered Geographic Information

Resilience in the urban context can be described as a continuum of absorptive, adaptive, and transformative capacities. The need to move toward a sustainable future and bounce forward after any disruption has led recent urban resilience initiatives to engage with the concept of transformative resilience when and where conventional and top-down resilience initiatives are less likely to deliver effective strategies, plans, and implementable actions. Transformative resilience pathways emphasize the importance of reflexive governance, inclusive co-creation of knowledge, innovative and collaborative learning, and self-organizing processes. To support these transformative pathways, considering techno-social co-evolution and digital transformation, using new data sources such as Volunteered Geographic Information (VGI) and crowdsourcing are being promoted. However, a literature review on VGI and transformative resilience reveals that a comprehensive understanding of the complexities and capacities of utilizing VGI for transformative resilience is lacking. Therefore, based on a qualitative content analysis of available resources, this paper explores the key aspects of using VGI for transformative resilience and proposes a comprehensive framework structured around the identified legal, institutional, social, economic, and technical aspects to formalize the process of adopting VGI in transformative resilience initiatives.

VGI-based spatial data infrastructure for land administration

The general public and land administration organizations have long recognized the need to establish and record legal ownership of property and land. To create land ownership records, developing countries have recently begun to public participation methods. For this purpose, a move to the Spatial Data Infrastructure (SDI) to aggregate heterogeneous geospatial information about the property and create interoperability between organizations and stakeholders is unavoidable. Solutions for public participation must also be provided. Designing and developing a model under the SDI policy, based on an open-source architecture, and based on volunteered geographic information (VGI) and citizen-wide participation in collecting rights-based spatial data is the subject of this study. Volunteered Rights-based Spatial Data Infrastructure (VRSDI) is a framework that was proposed and implemented in this study to help with the low-cost, high-speed voluntary collection of land and property legal information and land management in Iran. VRSDI assists people in defining land and property ownership rights, detecting potential disputes, seizures, or conflicts with neighbors, as well as their limits and obligations for land management through rights-based spatial data. VRSDI was conceptualized and implemented using a Software as a Service (SaaS) approach, including solutions for data and service quality control based on the Open Geospatial Consortium (OGC) standards. It benefits from an open-source multi-tier architecture and powerful components, including the AJAX engine, the VGI engine, the service adapter, and the Workflow engine. Also discussed is policymaking for the effective adoption of VGI in a VRSDI and solutions for spatial accuracy measurement and quality control. The findings demonstrated that an approach like this might lead to integrated land management by including citizens and the government.

A Risk-Based Approach for Enhancing the Fitness of Use of VGI

Volunteered Geographic Information (VGI) phenomena offer an alternative or supplement to the authoritative mechanism of geospatial data acquisition. It aims to allow people without professional geospatial skills or knowledge to participate in the geospatial data collection. VGI has been boosted by recent advances in geospatial technology and applications. VGI applications have shown great potential in various areas such as disaster management and public health. However, VGI suffers from a lack of quality assurance, because VGI contributors may lack knowledge of the geospatial domain and credibility. Moreover, VGI data may have different levels of detail and precision, and may have been collected for different purposes. Appropriate VGI data for a specific application may be less appropriate for another application. End-users may use VGI data without being aware of its appropriateness to their requirements. This may cause a risk that arises when end-users inappropriately use, or be uncertain about using VGI data in their applications. This risk may undermine the VGI project, in which end-users are involved. This paper proposes an approach that aims to enhance VGI quality assurance by measuring the spatio-semantic similarity between user requirements and provided VGI, and to evaluate the fitness-for-use of VGI. The proposed approach is based on an algorithm to help VGI end-users to deal with the risks related to the quality of VGI data. The approach helps end-user make appropriate decisions about VGI data (e.g., considering or not considering VGI data and being careful when using VGI data).

Maps, volunteered geographic information (VGI) and the spatio-discursive construction of nature

This paper interrogates the role that spatial media such as maps and Volunteered Geographic Information (VGI) play in the construction and mobilisation of representations of nature. Drawing on poststructural political ecology, critical cartography, and GIScience, this article engages maps and VGI as discursive mechanisms that solidify and convey meanings and representations of nature tied to broader strategies of commodification. Particularly, we explore how spatial media reproduces and legitimises discursive strategies that rationalise the reconciliation of economic development and conservation through nature-based tourism by producing new ways of nature commodification. Drawing on evidence from Patagonia-Aysén, Chile, this paper examines the intersections between the discourse of nature encoded within institutional tourist maps and advertisements, and within the VGI platform for travellers, TripAdvisor. This illustrative case shows, firstly, how tourist maps and advertisements have contributed to normalising a discursive construction of nature as pristine, grandiose, sublime and wild that has not only secured aesthetics as ontological qualities of nature, but also as embedded values that protect ‘nature’ as a commodity to consume. Secondly, our findings evidence that TripAdvisor emerges out of this context as content that mobilises individual perceptions of and narratives about Patagonian nature that is already mediated by this dominant discourse. This dynamic suggests that VGI constitutes a new form of discursive power that digitally reproduces and mobilises a dominant discourse of nature, (re)producing what we term ‘discursive digital nature’.

ВЫЯВЛЕНИЕ ГРАНИЦ ВНУТРИГОРОДСКИХ ВЕРНАКУЛЯРНЫХ РАЙОНОВ ПО ОНЛАЙН-ДАННЫМ НА ПРИМЕРЕ ГОРОДА САН-ФРАНЦИСКО

This article focuses on the identification of one of the forms of territorial organization of society – vernacular region in urban area (UVR), case-study of San-Francisco. In the course of the study, was analyzed a wide layer of foreign and domestic sources of the concept of vernacular regions, as well as its various interpretations. Author made an attempt to distinguish between the concepts of «neighborhood» and «vernacular region in urban area». Relevance and innovativeness of this work stem from the use of volunteered geographical information (VGI) as a data source, exactly social network Instagram. In the course of the study, the main methodological approaches were formulated – one online method for determining the boundaries of the UVR was developed and described, and its advantages and dis- advantages were also identified. As a result, the boundaries of the San Francisco UVR are delineated. Also, the identified boundaries are compared with the administrative boundaries of the administrative districts of the city (neighborhoods). For clarity of this comparison, maps have been compiled. Visible discrepancies are analyzed, lacunas are found in the urban vernacular grid of San Francisco. At the end of the study, the author explains the similarities and differences between the two concepts of «neighbourhood» and «vernacular region in urban area», and also suggests which of the elevations is best suited for specific tasks.

Mapping the surrounding environment by pupils. Case study: Primary school “Ismail Qemali” in Chair - Skopje

Abstract. Cartography in primary schools in the Republic of North Macedonia is present as part of geography subject, for four years from sixth to ninth level, per two hours weekly. Program is limited only on usage of paper maps aimed for learning geospatial phenomena, without information for map making process.Step forward toward increasing the awareness for including mapping in practical part of curricula in geography and other related subjects, are the activities undertaken by the Geo-SEE Institute from Skopje, by giving practical lectures to pupils in Primary School “Ismail Qemali” in Chair municipality, for usage the digital cartography tools via GIS software.Training was designed to be used FOSS for GIS and open geospatial data by teachers and pupils. Field identification and collection of geospatial data based on ortho images and other base materials, as well usage of smart phones have been used as supplementary methodology for establishing geospatial database aimed for map compilation. Voluntary geographic information and crowd sourcing methodologies as opportunities for usage in teaching and learning process in primary schools not only for geography but for all subjects that intersects with geospatial information, were explained to attendees. Within very short period of one and half month, before pandemic on march 2020, pupils achieved to work with basic tools of QGIS software, as well to compile two maps, one geographical map of North Macedonia, and a map of neighbourhood “Topansko Pole (Fushë Topanë)” as city map of the settlement in which the primary school is located.

Exploring five indicators for the quality of OpenStreetMap road networks: a case study of Québec, Canada

OpenStreetMap (OSM) is one of the most well-known volunteered geographic information (VGI) projects that aims to produce a free-world map. However, there are serious concerns about its quality. Numerous studies have assessed the quality of OSM by comparing the OSM database with a reference database. Several researchers have proposed the use of quality indicators as variables that can describe OSM quality in regions where no reference data are available. A quality indicator is a variable that has a significant monotonic relationship with quality measures. In this study, a literature review was conducted to identify and define the main quality measures proposed for assessing the quality of linear features. Owing to limited access to current data, only three quality elements—completeness, positional accuracy, and attribute accuracy—were evaluated in this study. These quality measures were then used to assess the quality of the OSM roads in the province of Quebec. Finally, Spearman’s rank correlation coefficient test was applied to determine whether there was a significant correlation between the quality measures related to the three quality elements and the five potential quality indicators: population, average income, density of OSM roads, density of OSM buildings, and number of points of interest (POI). The main contribution of this study is testing the following hypothesis: “There is a significant correlation between the five mentioned variables and the measures related to the three quality elements”. Statistical analysis showed that in terms of completeness, the density of OSM roads and population were the best indicators; in terms of positional accuracy, population and income were the best indicators; and in terms of attribute accuracy, completeness was the best indicator. All five variables have significant correlations with the measures of the three elements of quality, except for the following two pairs (attribute accuracy, density of OSM roads) and (attribute accuracy, density of OSM buildings). This study proposes the density of OSM roads and number of POI as two new quality indicators that have not been found in the literature review.