Public Participation GIS for sustainable urban mobility planning: methods, applications and challenges

Sustainable mobility planning is a new approach to planning, and as such it requires new methods of public participation, data collection and data aggregation. In the article we present an overview of Public Participation GIS (PPGIS) methods with potential use in sustainable urban mobility planning. We present the methods using examples from two recent case studies conducted in Polish cities of Poznań and Łodź. Sustainable urban mobility planning is a cyclical process, and each stage has different data and participatory requirements. Consequently, we situate the PPGIS methods in appropriate stages of planning, based on potential benefits they may bring into the planning process. We discuss key issues related to participant recruitment and provide guidelines for planners interested in implementing methods presented in the paper. The article outlines future research directions stressing the need for systematic case study evaluation

CommuniSense: Crowdsourcing Road Hazards in Nairobi

Nairobi is one of the fastest growing metropolitan cities and a major business and technology powerhouse in Africa. However, Nairobi currently lacks monitoring technologies to obtain reliable data on traffic and road infrastructure conditions. In this paper, we investigate the use of mobile crowdsourcing as means to gather and document Nairobi's road quality information. We first present the key findings of a city-wide road quality survey about the perception of existing road quality conditions in Nairobi. Based on the survey's findings, we then developed a mobile crowdsourcing application, called CommuniSense, to collect road quality data. The application serves as a tool for users to locate, describe, and photograph road hazards. We tested our application through a two-week field study amongst 30 participants to document various forms of road hazards from different areas in Nairobi. To verify the authenticity of user-contributed reports from our field study, we proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to verify whether submitted reports indeed depict road hazards. We found 92% of user-submitted reports to match the MTurkers judgements. While our prototype was designed and tested on a specific city, our methodology is applicable to other developing cities.Comment: In Proceedings of 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2015

Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki

Information about land cover and land use is needed for a wide range of applications such as nature protection and biodiversity, forest and water management, urban and transport planning, natural hazard prevention and mitigation, monitoring of agricultural policies and economic land use modelling. A number of different remotely-sensed global land cover products are available but studies have shown that there are large spatial discrepancies between these different products when compared. To address this issue of land cover uncertainty, a tool called Geo-Wiki was developed, which integrates online and mobile applications, high resolution satellite imagery available from Google Earth, and data collection through crowdsourcing as a mechanism for validating and improving globally relevant spatial information on land cover and land use. Through its growing network of volunteers and a number of successful data collection campaigns, almost 5 million samples of land cover and land use have been collected at many locations around the globe. This paper provides an overview of the main features of Geo-Wiki, and then using a series of examples, illustrates how the crowdsourced data collected through Geo-Wiki have been used to improve information on land cover and land use

Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places

New research cutting across architecture, urban studies, and psychology is contextualizing the understanding of urban spaces according to the perceptions of their inhabitants. One fundamental construct that relates place and experience is ambiance, which is defined as "the mood or feeling associated with a particular place". We posit that the systematic study of ambiance dimensions in cities is a new domain for which multimedia research can make pivotal contributions. We present a study to examine how images collected from social media can be used for the crowdsourced characterization of indoor ambiance impressions in popular urban places. We design a crowdsourcing framework to understand suitability of social images as data source to convey place ambiance, to examine what type of images are most suitable to describe ambiance, and to assess how people perceive places socially from the perspective of ambiance along 13 dimensions. Our study is based on 50,000 Foursquare images collected from 300 popular places across six cities worldwide. The results show that reliable estimates of ambiance can be obtained for several of the dimensions. Furthermore, we found that most aggregate impressions of ambiance are similar across popular places in all studied cities. We conclude by presenting a multidisciplinary research agenda for future research in this domain

A flexible framework for assessing the quality of crowdsourced data

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Crowdsourcing as a means of data collection has produced previously unavailable data assets and enriched existing ones, but its quality can be highly variable. This presents several challenges to potential end users that are concerned with the validation and quality assurance of the data collected. Being able to quantify the uncertainty, define and measure the different quality elements associated with crowdsourced data, and introduce means for dynamically assessing and improving it is the focus of this paper. We argue that the required quality assurance and quality control is dependent on the studied domain, the style of crowdsourcing and the goals of the study. We describe a framework for qualifying geolocated data collected from non-authoritative sources that enables assessment for specific case studies by creating a workflow supported by an ontological description of a range of choices. The top levels of this ontology describe seven pillars of quality checks and assessments that present a range of techniques to qualify, improve or reject data. Our generic operational framework allows for extension of this ontology to specific applied domains. This will facilitate quality assurance in real-time or for post-processing to validate data and produce quality metadata. It enables a system that dynamically optimises the usability value of the data captured. A case study illustrates this framework

Supporting Earth-Observation Calibration and Validation: A new generation of tools for crowdsourcing and citizen science

Citizens are providing vast amounts of georeferenced data in the form of in situ data collections as well as interpretations and digitization of Earth-observation (EO) data sets. These new data streams have considerable potential for supporting the calibration and validation of current and future products derived from EO. We provide a general introduction to this growing area of interest and review existing crowdsourcing and citizen science (CS) initiatives of relevance to EO. We then draw upon our own experiences to provide case studies that highlight different types of data collection and citizen engagement and discuss the various barriers to adoption. Finally, we highlight opportunities for how citizens can become part of an integrated EO monitoring system in the framework of the European Union (EU) space program, including Copernicus and other monitoring initiatives