Crowdsourced Geographic Information Research Articles

Association and Interaction Between Built Environment and Outdoor Jogging Based on Crowdsourced Geographic Information

Association and Interaction Between Built Environment and Outdoor Jogging Based on Crowdsourced Geographic Information

Livability assessment of 101,630 communities in China's major cities: A remote sensing perspective.

Some of China’s major cities have entered the middle and late stages of urbanization, and the development focus of these cities has gradually shifted from outward expansion to inward renewal. The community, as the basic unit of a city, is undoubtedly the main object of urban renewal. In order to efficiently and effectively address the problems in current community construction, it is necessary to conduct a large-scale in-depth assessment of the community livability, which can directly imply the satisfaction of residents with their quality of life. This study achieved the first comprehensive livability assessment at the individual community scale for 42 major cities of China from the perspective of remotely sensed and crowd-sourced geographic information. Specifically, we produced abundant fine-grained datasets for 42 cities, including high-resolution land cover maps interpreted from Ziyuan-3 satellites (ZY-3, 2.1 m), building height, point-of-interest, and boundaries of 101,630 communities. As designed in our proposed framework, the community livability was evaluated by 5 level-1 indicators, 27 level-2 indicators and an integrated community livability index (CLI). A number of interesting findings were obtained from this assessment: (1) According to the expert questionnaires, living comfort was considered as the most important livability factor for residents with the highest weight, while the building environment was rated the least. The negative factors (e.g., the factories around the community) impacted more on livability than the positive ones. (2) Most communities in major Chinese cities were characterized by dense buildings and sparse green spaces. (3) In these cities, community security construction was severely insufficient, particularly in less developed regions. (4) Imbalanced community livability development was prevalent across cities, and simultaneously, the CLI distribution within cities also exhibited significant spatial aggregation and heterogeneity. This research is expected to reveal the status quo of community livability in China, and thus allow for targeted policy formulation.Electronic Supplementary MaterialSupplementary material is available for this article at 10.1007/s11430-021-9896-4 and is accessible for authorized users.

Detecting inconsistent information in crowd-sourced street networks based on parallel carriageways identification and the rule of symmetry

Crowd-sourced geographic information has great potential in scientific and public domains, and is recently under consideration by geospatial professionals as an alternative to traditional spatial data collection. The success, however, implies a need to build long-term reliance on the crowd-sourcing projects, and poses growing concern over the quality of the constantly evolving data. In general, we aim to develop an approach that uses geographic rules to identify inconsistent information in street networks without relying on external sources. This paper focuses on a more challenging sub-process that aims to identifying inconsistent information using the rule of symmetry. That is, information (e.g. name, class, speed limit, etc.) in parallel carriageways (e.g. divided highways) always constrains each other. The process starts with a clustering of related streets into well-defined or ambiguous situations using a DBSCAN-inspired technique; then two pairing strategies are designed for both situations. To address the challenging problem of pairing carriageways in ambiguous situations, three pairing algorithms (stroke-based, tree-based, and mixed) are devised based on the idea of using expanded ‘receptive field’ to disentangle the ambiguities; each has a focus on efficiency, effectiveness, or their tradeoff. Evaluating the algorithms against 7 selected datasets shows that all three algorithms reached satisfactory performance (F1-score > 92%) for ambiguous situations, and much higher accuracy for the whole datasets. Then, we applied our approach to over 40 datasets worldwide and detected inconsistencies (i.e. dissimilar values in paired carriageways) in crowd-sourced and authoritative street networks. We evaluate the identified inconsistencies, analyze the possibilities of our approach in suggesting corrections to problematic data, and discuss its effectiveness, issues, and future directions. We thereby demonstrate that the proposed approach is effective for quality assurance, and can be used to assure the quality of crowd-sourced and authoritative mapping projects during their evolution without relying on ground-truth.

Evolution, density and completeness of OpenStreetMap road networks in developing countries: The case of Iran

OpenStreetMap (OSM) is a volunteered platform designed to provide up-to-date and freely available geographic information data. OSM is known as one of the most extensively used instances of Crowdsourcing Geographic Data (CSGD)/Crowdsourced Geographic Information (CGI), Volunteered Geographic Information (VGI), or the Neogeography paradigm. Economically, OSM development can be beneficial for governments, especially in developing countries such as Iran, where financial support is limited. This paper analyzes the spatial pattern, evolution, density, and diversity of OSM road (OSMr) networks in Iran between 2008 and 2016 and looks to find casual relations between the OSM and census statistics. This is due to the fact that OSMr completeness reflects the importance of OSM data in human life. The diversity of OSM roads further reflects the concerns, requirements, and worthiness of clients about different roads’ information. The results show that the road network in Iran considerably increased from 2008 to 2016, with road length increasing to 489,400 km in 2016 from 4300 km in 2008. In addition, road density grew while road diversity and evenness declined, which could be due to an increase in main roads. These results propose a more considerable need for a more comprehensive approach using VGI to supplement gaps in authoritative data in developing countries.

Exploring the use of crowdsourced geographic information in defence: challenges and opportunities

Geographic data are used by United Kingdom (UK) defence for purposes including peacekeeping, humanitarian aid and disaster relief, and fighting wars. The geographic extent of defence data covers the world, with greater focus directed towards areas considered to be of current interest. Traditionally, these data have been officially sourced, e.g. via National Mapping Agencies, but there is now increasing interest in the potential of crowdsourced geographic data to supplement authoritative data where they are not available, outdated or incomplete. Volunteered geographic information (VGI) and social media have the potential to provide this needed missing information. This paper presents initial work carried out in identifying the potential of crowdsourced geographic information in defence. We first provide a short description of the role of UK defence and review the existing literature on crowdsourced geographic information in defence, as well as generic VGI quality assessment methods. We then explore the potential of crowdsourced data in real-world applications: the conflation of VGI and social media with official data for effective decision-making in war zones, and the potential for crowdsourcing to increase effective collaboration between machines and humans in disaster situations. Based on our review, we outline specific research challenges for deploying crowdsourced geographic information in defence, focussing on data quality and fitness-for-purpose assessment. Defence-specific constraints include the need for rapid quality assessment processes and the need to communicate high-quality information effectively in situations where rapid decision-making is required. Ethical issues are also of fundamental importance.

Change Detection for Building Footprints with Different Levels of Detail Using Combined Shape and Pattern Analysis

Crowd-sourced geographic information is becoming increasingly available, providing diverse and timely sources for updating existing spatial databases to facilitate urban studies, geoinformatics, and real estate practices. However, the discrepancies between heterogeneous datasets present challenges for automated change detection. In this paper, we identify important measurable factors to account for issues like boundary mismatch, large offset, and discrepancies in the levels of detail between the more current and to-be-updated datasets. These factors are organized into rule sets that include data matching, merge of the many-to-many correspondence, controlled displacement, shape similarity, morphology of difference parts, and the building pattern constraint. We tested our approach against OpenStreetMap and a Dutch topographic dataset (TOP10NL). By removing or adding some components, the results show that our approach (accuracy = 0.90) significantly outperformed a basic geometric method (0.77), commonly used in previous studies, implying a more reliable change detection in realistic update scenarios. We further found that distinguishing between small and large buildings was a useful heuristic in creating the rules.

A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information.

The growing use of crowdsourced geographic information (CGI) has prompted the employment of several methods for assessing information quality, which are aimed at addressing concerns on the lack of quality of the information provided by non‐experts. In this work, we propose a taxonomy of methods for assessing the quality of CGI when no reference data are available, which is likely to be the most common situation in practice. Our taxonomy includes 11 quality assessment methods that were identified by means of a systematic literature review. These methods are described in detail, including their main characteristics and limitations. This taxonomy not only provides a systematic and comprehensive account of the existing set of methods for CGI quality assessment, but also enables researchers working on the quality of CGI in various sources (e.g., social media, crowd sensing, collaborative mapping) to learn from each other, thus opening up avenues for future work that combines and extends existing methods into new application areas and domains.

Digital footprints: Incorporating crowdsourced geographic information for protected area management

Biodiversity loss driven by anthropogenic pressures highlights the importance of conservation efforts in protected areas globally. Protected areas are also locations providing myriad ecosystem services, including recreation and tourism. Advancements in mobile and web technologies have expanded the capabilities and accessibility of crowdsourced spatial content increasingly leveraged for research. This study explores the use of crowdsourced geographic information to model, at varying temporal scales, spatial patterns of visitor use and identify factors contributing to distribution patterns in a dynamic landscape, Hawaii Volcanoes National Park (Hawaii, USA). Specifically, this study integrated geotagged photo metadata publicly shared on Flickr with raster data about infrastructure and natural environmental using MaxEnt modelling. Infrastructure designated for visitor use (i.e., roads, trails) contributed most to models of visitor distribution for all years and seasons. During the spring months, elevation was also a top contributing variable to the model. Crowdsourced data provided empirical assessments of covariates associated with visitor distributions, highlighting how changes in infrastructure and environmental factors may influence visitor use, and therefore resource pressures, to help researchers, managers, and planners with efforts to mitigate negative impacts.

Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China

Land cover change (LCC) detection is a significant component of sustainability research including ecological economics and climate change. Due to the rapid variability of natural environment, effective LCC detection is required to capture sufficient change-related information. Although such information has been available through remotely sensed images, the complicated image processing and classification make it time consuming and labour intensive. In contrast, the freely available crowdsourced geographic information (CGI) contains easily interpreted textual information, and thus has the potential to be applied for capturing effective change-related information. Therefore, this paper presents and evaluates a method using CGI for rapid LCC detection. As a case study, Beijing is chosen as the study area, and CGI is applied to monitor LCC information. As one kind of CGI which is generated from commercial Internet maps, points of interest (POIs) with detailed textual information are utilised to detect land cover in 2016. Those POIs are first classified into land cover nomenclature based on their textual information. Then, a kernel density approach is proposed to effectively generate land cover regions in 2016. Finally, with GlobeLand30 in 2010 as baseline map, LCC is detected using the post-classification method in the period of 2010–2016 in Beijing. The result shows that an accuracy of 89.20% is achieved with land cover regions generated by POIs, indicating that POIs are reliable for rapid LCC detection. Additionally, an LCC detection comparison is proposed between remotely sensed images and CGI, revealing the advantages of POIs in terms of LCC efficiency. However, due to the uneven distribution, remotely sensed images are still required in areas with few POIs.

Exploiting crowdsourced geographic information and GIS for assessment of air pollution exposure during active travel

Improvement on assessment of air pollution exposure will enhance assessment of health risk-benefit when active travel (cycling and walking). Earlier studies assessed air pollution exposure according to travel time and city-level air pollution. The lack of spatially fine-grained travel data is a barrier to an accurate assessment of air pollution exposure. Due to a high-level spatial granularity, Strava Metro provides an opportunity to assessing air pollution exposure in combination with spatially varying air pollution concentrations. Strava Metro anonymized and aggregated a large volume of users’ traces to streets for each city. In this study, to explore the potential of crowdsourced geographic information in research of active travel and health, we used Strava Metro data and GIS technologies to assess air pollution exposure in Glasgow, UK. Particularly, we incorporated time of the trip to assess average inhaled dose of pollutant during a single cycling or pedestrian trip. Empirical results demonstrate that Strava Metro data provides an opportunity to an assessment of average air pollution exposure during active travel. Additionally, to demonstrate the potential of Strava Metro data in policy-making, we explored the spatial association of air pollution concentration and active travel. As a result, we identified areas that require investment priority, and finally offered implications for policies.

Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model

Land cover classification is the most important element of land cover mapping and is a key input to many societal benefits. Traditional classification methods require a large amount of remotely sensed images, which are time consuming and labour intensive. Recently, crowdsourcing geographic information (CGI), including geo-tagged photos and other sources, has been widely used with lower costs, but still requires extensive labour for data classification. Alternatively, CGI textual information is available from online sources containing land cover information, and it provides a useful source for land cover classification. However, the major challenge of utilising CGI is its uneven spatial distributions in land cover regions, leading to less reliability of regions for land cover classification with sparsely distributed CGI. Moreover, classifying various unorganised CGI texts automatically in each land cover region is another challenge. This paper investigates a faster and more automated method that does not require remotely sensed images for land cover classification. Spatial clustering is employed for CGI to reduce the effect of uneven spatial distributions by extracting land cover regions with high density of CGI. To classify unorganised various CGI texts in each extracted region, land cover topics are calculated using topic model. As a case study, we applied this method using points of interest (POIs) as CGI to classify land cover in Shandong province. The classification result using our proposed method achieved an overall accuracy of approximately 80%, providing evidence that CGI with textual information has a great potential for land cover classification.

Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information

Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of ~100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject.

The Countercultural Potential of Citizen Science

What is the countercultural potential of citizen science? As a participant in the wider citizen science movement, I can attest that contemporary citizen science initiatives rarely characterise themselves as countercultural. Rather, the goal of most citizen science projects is to be seen as producing orthodox scientific knowledge: the ethos is respectability rather than rebellion (NERC). I will suggest instead that there are resonances with the counterculture that emerged in the 1960s, most visibly through an emphasis on participatory experimentation and the principles of environmental sustainability and social justice. This will be illustrated by example, through two citizen science projects that have a commitment to combining social values with scientific practice. I will then describe the explicitly countercultural organisation, Science for the People, which arose from within the scientific community itself, out of opposition to the Vietnam War. Methodological and conceptual weaknesses in the authoritative model of science are explored, suggesting that there is an opportunity for citizen science to become anti-hegemonic by challenging the hegemony of science itself. This reformulation will be expressed through Deleuze and Guattari's notion of nomadic science, the means through which citizen science could become countercultural.

GIS and agent-based models for humanitarian assistance

Natural disasters such as earthquakes and tsunamis occur all over the world, altering the physical landscape and often severely disrupting people’s daily lives. Recently researchers’ attention has focused on using crowds of volunteers to help map the damaged infrastructure and devastation caused by natural disasters, such as those in Haiti and Pakistan. This data is extremely useful, as it is allows us to assess damage and thus aid the distribution of relief, but it tells us little about how the people in such areas will react to the devastation. This paper demonstrates a prototype spatially explicit agent-based model, created using crowdsourced geographic information and other sources of publicly available data, which can be used to study the aftermath of a catastrophic event. The specific case modelled here is the Haiti earthquake of January 2010. Crowdsourced data is used to build the initial populations of people affected by the event, to construct their environment, and to set their needs based on the damage to buildings. We explore how people react to the distribution of aid, as well as how rumours relating to aid availability propagate through the population. Such a model could potentially provide a link between socio-cultural information about the people affected and the relevant humanitarian relief organizations.