The impact of society on volunteered geographic information: The case of OpenStreetMap

Volunteered Geographical Information (VGI) has been extensively studied in terms of its quality and completeness in the past. However, little attention is given to understanding what factors, beyond individuals’ expertise, contribute to the success of VGI. In this chapter we ask whether society and its characteristics such as socio-economic factors have an impact on what part of the physical world is being digitally mapped. This question is necessary, so to understand where crowd-sourced map information can be relied upon (and crucially where not), with direct implications on the design of applications that rely on having complete and unbiased map knowledge. To answer the above questions, we study over 6 years of crowd-sourced contributions to OpenStreetMap (OSM) a successful example of the VGI paradigm. We measure the positional and thematic accuracy as well as completeness of this information and quantify the role of society on the state of this digital production. Finally we quantify the effect of social engagement as a method of intervention for improving users’ participation

Not at Home on the Range: Peer Production and the Urban/Rural Divide

ABSTRACT Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. In this paper, we explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in both Wikipedia and OpenStreetMap, peer-produced content about rural areas is of systematically lower quality, is less likely to have been produced by contributors who focus on the local area, and is more likely to have been generated by automated software agents (i.e. "bots"). We then codify the systemic challenges inherent to characterizing rural phenomena through peer production and discuss potential solutions

Not at Home on the Range: Peer Production and the Urban/Rural Divide

ABSTRACT Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. In this paper, we explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in both Wikipedia and OpenStreetMap, peer-produced content about rural areas is of systematically lower quality, is less likely to have been produced by contributors who focus on the local area, and is more likely to have been generated by automated software agents (i.e. "bots"). We then codify the systemic challenges inherent to characterizing rural phenomena through peer production and discuss potential solutions

Project Sidewalk: A Web-based crowdsourcing tool for collecting sidewalk accessibility data at scale

Ministry of Education, Singapore under its Academic Research Funding Tier

Quality Assessment of the Canadian OpenStreetMap Road Networks

Volunteered geographic information (VGI) has been applied in many fields such as participatory planning, humanitarian relief and crisis management because of its cost-effectiveness. However, coverage and accuracy of VGI cannot be guaranteed. OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of geospatial data quality in OSM has become a trending research topic because of the large size of the dataset and the multiple channels of data access. The objective of this study is to examine the overall reliability of the Canadian OSM data. A systematic review is first presented to provide details on the quality evaluation process of OSM. A case study of London, Ontario is followed as an experimental analysis of completeness, positional accuracy and attribute accuracy of the OSM street networks. Next, a national study of the Canadian OSM data assesses the overall semantic accuracy and lineage in addition to the quality measures mentioned above. Results of the quality evaluation are compared with associated OSM provenance metadata to examine potential correlations. The Canadian OSM road networks were found to have comparable accuracy with the tested commercial database (DMTI). Although statistical analysis suggests that there are no significant relations between OSM accuracy and its editing history, the study presents the complex processes behind OSM contributions possibly influenced by data import and remote mapping. The findings of this thesis can potentially guide cartographic product selection for interested parties and offer a better understanding of future quality improvement in OSM

Joukkoistetun paikkatiedon laatu ja luotettavuus : Turun kaupunkiseudun pyöräilyverkosto OpenStreetMap-karttapalvelussa

Kansalaislähtöinen joukkoistettu paikkatieto on noussut varteenotettavaksi tietolähteeksi viranomaisten ja kaupallisen sektorin tuottaman auktoritatiivisen paikkatiedon rinnalle. Tuotantotavat poikkeavat toisistaan merkittävästi, mikä ilmenee joukkoistetun lopputuotteen laatuvaihteluna. Sitä aiheuttavat tavoitteiltaan ja taitotasoltaan kirjava kartoittajajoukko, tuotantoalustojen ominaisuudet ja avoin tuotantotapa, joka ei pakota vaatimustenmukaisuutta. Jotta joukkoistettua paikkatietoa voidaan luotettavasti hyödyntää auktoritatiivisessa yhteydessä, on ymmärrettävä sen laadullisia piirteitä. Niitä koskevaa kotimaista tutkimustietoa on vähän ja tiedon tarve on uusiutuva joukkoistetun paikkatiedon jatkuvasti päivittyessä ja rikastuessa teemoiltaan. Tutkielmassa tähän tarpeeseen pyrittiin vastaamaan vertaamalla joukkoistetun OpenStreetMap-karttapalvelun pyöräilyverkostotietoa Turun kaupungin ja sen ympäryskuntien ylläpitämän paikkatietoaineiston laatuvaatimuksiin. Laatu mitattiin ISO 19157:2013-standardin mukaisesti täydellisyyden, sijaintitarkkuuden ja topologisen eheyden laatuparametreille. Niitä määrällistettiin verkoston kokonaispituudella, keskimääräisellä etäisyydellä ja geometrisella yhdenmukaisuudella sekä kappalemääräisellä laskennalla. Alueellista vaihtelua arvioitiin visuaalisesti, keskihajonnalla ja tilastollisesti Anselinin paikallisella Moranin I -tunnusluvulla. Koska paikkatietoaineiston kokonaislaadun muodostaa kvantitatiivisen laadun lisäksi sopivuus käyttötarkoitukseen ja lisäksi kokonaislaadun molempien komponenttien yhteisvaikutus luotettavuuteen on aiemmassa tutkimuksessa jäänyt vähälle huomiolle, aineiston käytettävyyttä arvioitiin pyöräilyverkoston kartoitustarpeiden näkökulmasta laatuarvioon perustuen. Luotaava käytettävyysmittaus painotti pyöräilyverkoston kartoitustarpeille kriittisiä laatuparametreja käytettävyyttä ilmaisevassa aggregaatissa. Joukkoistetun paikkatiedon laatuun vaikuttavat tekijät ovat osin tilasidonnaisia. Tätä tutkittiin laatu- ja käytettävyysarvioiden tulosten alueellisen vaihtelun arviolla. OpenStreetMap-aineiston täydellisyyden ja sijaintitarkkuuden havaittiin olevan lähellä laatuvaatimusta ja osin täyttävän sen. Topologinen eheys osoittautui sen sijaan heikoksi. Käytettävyysmittaus puolestaan osoitti virheettömyyden vaatimuksen korostuvan sovellustarpeiden monimutkaistuessa. Sama aineisto voi soveltua hyvin yhteen käyttötarkoitukseen, mutta olla kelvoton toiseen. Kvantitatiivisen laadun ja käytettävyyden suuri alueellinen vaihtelu heikensi selvästi aineiston luotettavuutta. Vaikka laajoja yhtenäisiä hyvätasoisia alueita paikallisesti havaittiin, aineistotason luotettavuus oli välttävä. Tulokset osoittavat joukkoistetun paikkatiedon lunastavan osan sen hyödyntämiseen liittyvistä odotuksista, mutta sen luotettavuutta on vaikea ennakoida. Tämä viittaa laadunvarmistuksen jatkuvaan välttämättömyyteen sekä tarpeeseen asettaa laatumittauksen tavoitteet tapauskohtaisesti. Tällöin myös laatuparametreiltään tai alueellisesti osittain heikkolaatuista aineistoa voidaan käyttää sovelluskohteiden niin salliessa ja joukkoistetun paikkatiedon temaattisesta moninaisuudesta päästään erityyppisissä sovellusyhteyksissä hyötymään.Citizen-sourced volunteered geographic information (VGI) has emerged as a significant data source alongside authoritative spatial data produced by governmental agencies and the commercial sector. The modes of production differ substantially, resulting in variable quality in the crowdsourced final product. This variability stems from a diverse group of mappers in terms of objectives and skill levels, platform attributes, and an open production method that does not enforce conformity to standards. To reliably utilize crowdsourced spatial data in authoritative contexts, one must comprehend its quality attributes. There's a paucity of domestic research on this topic, and the need for information is recurrent, with VGI continually updating and diversifying in themes. This study aimed to address this gap by comparing the cycling network data from the crowdsourced OpenStreetMap service to the quality standards of spatial datasets maintained by the city of Turku and its surrounding municipalities. Quality was assessed based on the ISO 19157:2013 standard, focusing on completeness, positional accuracy, and topological integrity quality parameters. These were quantified by the total network length, average distance, geometric consistency, and count-based calculations. The regional variability was assessed both visually and statistically, using standard deviation and Anselin's local Moran's I index. Given that the overall quality of spatial datasets is not only defined by quantitative quality but also its suitability for intended use, and considering previous research has often overlooked the combined impact of these components on reliability, the usability of the dataset was evaluated from the perspective of cycling network mapping needs based on the quality assessment. This exploratory usability assessment emphasized quality parameters critical to cycling network mapping in an aggregate indicating usability. Factors influencing the quality of crowdsourced spatial data are partly location dependent. This was examined by analyzing the regional variability in the results of quality and usability assessments. The completeness and positional accuracy of the OpenStreetMap dataset were found to be close to the quality requirements and in some cases meeting them. However, topological integrity was notably weak. The usability assessment indicated an increased emphasis on the requirement for accuracy as application needs become more complex. The same dataset might be well-suited for one purpose but unsuitable for another. The significant regional variability in quantitative quality and usability markedly reduced the dataset's reliability. Even though extensive, consistently high-quality areas were observed locally, the dataset's overall reliability was found to be subpar. The results suggest that crowdsourced spatial data meets some of the expectations related to its utilization but remains a volatile data source in terms of reliability. This underscores the continuous necessity for quality assurance and the need to set quality assessment objectives on a case-by-case basis. This approach allows for the use of datasets that may be subpar in quality parameters or regional attributes, given the application permits, capitalizing on the thematic diversity of crowdsourced spatial data in various application contexts

Understanding Geographic Bias in Crowd Systems

University of Minnesota Ph.D. dissertation. December 2017. Major: Computer Science. Advisors: Loren Terveen, Brent Hecht. 1 computer file (PDF); ix, 159 pages.Crowd platforms are increasingly geographic, from the sharing economy to peer production systems like OpenStreetMap. Unfortunately, this means that existing geographic advantages or disadvantages (e.g. by income, urbanness, or race) may also impact these crowd systems. This thesis focuses on two primary themes: (1) how these geographic advantages and disadvantages interact with crowd platform services, and (2) how people’s geographic behavior within these platforms may lead to these biases being reflected. The first chapter in my thesis finds that sharing economy services fare less well in low-income, non-white, and more suburban areas. This chapter introduces the spatial Durbin model to the field of HCI, and shows that geographic factors like distance, socioeconomic status and demographics inform where sharing economy workers provide service. The second chapter in my thesis provides focuses on people in peer production communities contribute geographic content. By considering peer production as a spatial interaction process, this study finds that some kinds of content tend to be produced much more locally than others. Finally, my third contribution focuses on individual contributor behavior, and shows geographic “born, not made” trends. People tend to be consistent in the places, and kinds of places (urban, and non-high poverty counties) they contribute. The findings of this third study help identify mechanisms for how geographic biases may come about. Looking forward, my work helps inform an exciting agenda of future work, including building systems that provide individual crowd members sufficient geographic context to counteract these worrying geographic biases

Scalable Methods to Collect and Visualize Sidewalk Accessibility Data for People with Mobility Impairments

Poorly maintained sidewalks pose considerable accessibility challenges for people with mobility impairments. Despite comprehensive civil rights legislation of Americans with Disabilities Act, many city streets and sidewalks in the U.S. remain inaccessible. The problem is not just that sidewalk accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori. To address this problem, my Ph.D. dissertation introduces and evaluates new scalable methods for collecting data about street-level accessibility using a combination of crowdsourcing, automated methods, and Google Street View (GSV). My dissertation has four research threads. First, we conduct a formative interview study to establish a better understanding of how people with mobility impairments currently assess accessibility in the built environment and the role of emerging location-based technologies therein. The study uncovers the existing methods for assessing accessibility of physical environment and identify useful features of future assistive technologies. Second, we develop and evaluate scalable crowdsourced accessibility data collection methods. We show that paid crowd workers recruited from an online labor marketplace can find and label accessibility attributes in GSV with accuracy of 81%. This accuracy improves to 93% with quality control mechanisms such as majority vote. Third, we design a system that combines crowdsourcing and automated methods to increase data collection efficiency. Our work shows that by combining crowdsourcing and automated methods, we can increase data collection efficiency by 13% without sacrificing accuracy. Fourth, we develop and deploy a web tool that lets volunteers to help us collect the street-level accessibility data from Washington, D.C. As of writing this dissertation, we have collected the accessibility data from 20% of the streets in D.C. We conduct a preliminary evaluation on how the said web tool is used. Finally, we implement proof-of-concept accessibility-aware applications with accessibility data collected with the help of volunteers. My dissertation contributes to the accessibility, computer science, and HCI communities by: (i) extending the knowledge of how people with mobility impairments interact with technology to navigate in cities; (ii) introducing the first work that demonstrates that GSV is a viable source for learning about the accessibility of the physical world; (iii) introducing the first method that combines crowdsourcing and automated methods to remotely collect accessibility information; (iv) deploying interactive web tools that allow volunteers to help populate the largest dataset about street-level accessibility of the world; and (v) demonstrating accessibility-aware applications that empower people with mobility impairments

Leveraging the value of crowdsourced geographic information to detect cultural ecosystem services

Within ecological research and environmental management, there is current focus on demonstrating the links existing between human well-being and nature conservation. There is a need for better understanding how and why people value certain places over others. At the same time, there is a lack of consolidated methodologies, and limited experimentation in the detection of places connected to the immaterial benefits we get from nature. Those benefits are termed Cultural Ecosystem Services (CES). This research analyses the potential of Crowdsourced Geographic Information (CGI) to support the detection of CES with large scale insights derived from the analysis of digital cultural practices. CGI is produced through social media, in situations where individuals choose to share content. Therefore, a CGI project is often the expression of a community of interest and different projects have different supporting communities with different demographics and cultural profiles. The research combines multiple projects pertaining to three different categories of CGI to avoid focusing only on a community or on a digital cultural practice. Using ecological and social considerations, this thesis contributes to the evaluation of such projects as potential analytical tools for CES research. The degree of appreciation of a specific place is derived from the number of people creating, sharing, or refining the information about it. The sequence of decisions and actions that leads to the sharing of information leaves digital proxies of spatial preferences, with people sharing specific information considering the place not only “worth visiting” but also “worth sharing”. Using south Wales and London as case studies, we demonstrate how the analysis of CGI can be included in methodologies used to detect CES. These results highlight how the inclusion of CGI, can be very effective in addressing some of the current priorities in conservation. It could potentially be used for better prioritisation, planning and management of natural and cultural resources towards a more sustainable development