Mapping with Stakeholders: An Overview of Public Participatory GIS and VGI in Transport Decision-Making
Abstract
:1. Introduction
- Access and dissemination of spatial data, with the introduction of Geography Markup Language (GML), a data codification and transmission mechanism, the Scalable Vector Graphics (SVG), to improve the quality of the reproduction on the web, OGC geospatial web services and Spatial Data Infrastructures (SDIs), mechanisms with some specifics to improve accessibility and recover of spatial data [15].
- Exploration and geo-visualization of spatial data, with the introduction of maps for structuring the debate and achieving different levels of agreement among decision-making process actors: in a Web environment with GIS capability the map becomes dynamic, interactive and accessible to a wider number of users as a visual communication tool.
- Data elaboration, analysis and modeling, with the introduction of integrated tools between Web GIS and services to process data with intuitive interfaces, making modeling possible for both experts and general public users.
2. Guidelines through the Overview
- Description of the aim of overview;
- Delineation of the search strategy;
- Summary of key findings.
2.1. Aim/Question of Overview:
- Which subjects or kind of decisions are studied? In particular, an analysis of the transport studies per main categories and a distribution in sub-categories will be conducted.
- How is the GIS used to involve the participants? Based on the opportunity and level of use of the GIS tools by the participants.
- Who is the “public” involved in the study? This question aims at knowing the type of involvement in the study, if limited to a small group of users or to a large community.
- What is the stage of the transport decision-making process when GIS is used? This question aims at knowing whether VGI and PPGIS are suitable for use during the entire decision-making process or only in some of its phases.
2.2. Search Strategy
- Scopus (https://www.scopus.com), an online scientific abstract and citation database;
- Science-Direct (https://www.sciencedirect.com/), an online subscription-based database of scientific research, with freely available article abstracts, but subscription or pay-per-view purchase of their full texts;
- Web of Science (https://clarivate.com/products/web-of-science/), an online subscription-based scientific citation indexing, that gives access to multiple databases that reference cross-disciplinary research;
2.3. Summary of Key Findings
2.3.1. Statistical Analysis
2.3.2. Main Topics and Transport Mode Analysis
2.3.3. Use of GIS and Type of Involvement
2.3.4. Stage of the Transport Decision-Making Process
2.3.5. Conclusions
3. Three “Ingredients” for Performance and Consensus Based Decisions: A Framework for Transport Decision-Making
4. Conclusions and Future Research
Author Contributions
Funding
Conflicts of Interest
References
- EC—European Commission. Roadmap to a Single European Transport Area-Towards a Competitive and Resource Efficient Transport System. White Paper, Communication; EC: Brussels, Belgium, 2011; p. 144. [Google Scholar]
- Caprì, S.; Ignaccolo, M.; Inturri, G.; Le Pira, M. Green walking networks for climate change adaptation. Transp. Res. Part D 2016, 45, 84–95. [Google Scholar] [CrossRef]
- Le Pira, M. Towards Participatory Decision-Making Processes in Transport Planning: An Agent-Based Approach. Ph.D. Thesis, University of Catania, Catania, Italy, 2015. Available online: http://dspace.unict.it/handle/10761/3934 (accessed on 23 April 2019).
- Greco, S.; Figueira, J.; Ehrgott, M. Multiple Criteria Decision Analysis; Springer: New York, NY, USA, 2016. [Google Scholar]
- Hoksing, J.; Mudu, P.; Dora, C. Health Co-Benefits of Climate Change Mitigation—Transport Sector; World Health Organization: Geneva, Switzerland, 2011; ISBN 978 92 4 150291 7. [Google Scholar]
- Le Pira, M.; Inturri, G.; Ignaccolo, M.; Pluchino, A. Dealing with the complexity of stakeholder interaction in participatory transport planning. In Advanced Concepts, Methodologies and Technologies for Transportation and Logistics; (AISC, volume 572); Zak, J., Hadas, Y., Rossi, R., Eds.; Springer International Publishing: Chanm, Switzerland, 2018; pp. 54–72. [Google Scholar] [CrossRef]
- Le Pira, M. Transport planning with stakeholders: An agent-based modelling approach. Int. J. Transp. Econ. 2018, 45, 15–32. [Google Scholar]
- Sieber, R. Public participation geographic information systems: A literature review and framework. Ann. Assoc. Am. Geogr. 2006, 96, 491–507. [Google Scholar] [CrossRef]
- Obermeyer, N.J. The evolution of public participation in GIS. Cartogr. Geogr. Inf. Sci. 1998, 25, 65–66. [Google Scholar] [CrossRef]
- Schroeder, P. Criteria for the Design of a GIS/2. Specialists’ meeting for NCGIA Initiative 19: GIS and society, Summer 1996. Available online: http://www.spatial.maine.edu/~schroedr/ppgis/criteria.html (accessed on 29 January 2006).
- Kingston, R.; Smith, R.S. Who Are the Public and What Are They Participating in? World Universities Public Participation GIS Seminar Series. Available online: http://www.ppgis.manchester.ac.uk/downloads/WUN_GISc_PPGIS_Seminar.pdf (accessed on 23 April 2019).
- Plewe, B. GIS Online: Information Retrieval, Mapping, and the Internet; OnWord Press: Santa Fe, NM, USA, 1997. [Google Scholar]
- Green, D.; Bossomaier, T. Online GIS and Spatial Metadata; Taylor & Francis: London, UK, 2002. [Google Scholar]
- Peng, Z.R.; Tsou, M.H. Internet GIS: Distributed Geographic Information Services for the Internet and Wireless Networks; John Wiley & Sons: Hoboken, NJ, USA, 2003; p. 720. [Google Scholar]
- Peng, Z.R.; Zhang, C. The roles of geography markup language, scalable vector graphics, and web feature service specifications in the development of internet geographic information systems. J. Geogr. Syst. 2003, 6, 95–116. [Google Scholar] [CrossRef]
- Carver, S. Developing Web-based GIS/MCE: Improving access to data and spatial decision support tools. In Spatial Multicriteria Decision-Making and Analysis; Thill, J.C., Ed.; Ashgate: Aldershot, UK, 1999; pp. 49–75. [Google Scholar]
- Carver, S.; Peekham, R. Using GIS on the Internet for planning. In Geographical Information and Planning; Stillwell, J., Geertman, S., Openshaw, S., Eds.; Springer: Berlin, Germany, 1999; pp. 371–390. [Google Scholar]
- Dragićević, S. The potential of web-based GIS. J. Geogr. Syst. 2004, 6, 79–81. [Google Scholar] [CrossRef]
- Dragićević, S.; Balram, S. A web GIS collaborative framework to structure and manage distributed planning processes. J. Geogr. Syst. 2004, 6, 133–153. [Google Scholar] [CrossRef]
- Miller, C.C. A beast in the field: The Google Maps mashup as GIS/2. Cartographica 2006, 41, 187–199. [Google Scholar] [CrossRef]
- Biazzo, I.; Monechi, B.; Loreto, V. Universal scores for accessibility and inequalities in urban areas. arXiv 2018, arXiv:1810.03017. [Google Scholar]
- Cascetta, E.; Pagliara, F. Public engagement for planning and designing transportation systems. Pro. Soc. Behav. Sci. 2013, 87, 103–116. [Google Scholar] [CrossRef]
- Le Pira, M.; Ignaccolo, M.; Inturri, G.; Pluchino, A.; Rapisarda, A. Modelling stakeholder participation in transport planning. Case Stud. Transp. Policy 2016, 4, 230–238. [Google Scholar] [CrossRef]
- Goodchild, M.F. Citizens as sensors: the world of volunteered geography. GeoJournal 2007, 69, 211–221. [Google Scholar] [CrossRef]
- Verplanke, J.; McCall, M.K.; Uberhuaga, C.; Rambaldi, G.; Haklay, M. A shared perspective for PGIS and VGI. Cartogr. J. 2016, 53, 308–317. [Google Scholar] [CrossRef]
- Institute for Local Government. A Local Official’s Guide to Online Public Engagement. Available online: https://www.ca-ilg.org/sites/main/files/file-attachments/a_local_officials_guide_to_online_public_engagement_0.pdf (accessed on 23 April 2019).
- Nyerges, T.L.; Montejano, R.; Oshiro, C.; Dadswell, M. Group-based geographic information systems for transportation improvement site selection. Transp. Res. Part C Emerg. Technol. 1997, 5, 349–369. [Google Scholar] [CrossRef]
- Case, C.; Hawthorne, T.L. Served or unserved? A site suitability analysis of social services in Atlanta, Georgia using geographic Information systems. Appl. Geogr. 2013, 38, 96–106. [Google Scholar] [CrossRef]
- Laatikainen, T.; Tenkanen, H.; Kyttä, M.; Toivonen, T. Comparing conventional and PPGIS approaches in measuring equality of access to urban aquatic environments. Landsc. Urban Plan. 2015, 144, 22–33. [Google Scholar] [CrossRef]
- Stewart, A.F. Mapping transit accessibility: Possibilities for public participation. Transp. Res. Part A Policy Pract. 2017, 104, 150–166. [Google Scholar] [CrossRef]
- Oloo, F. Mapping rural road networks from Global Positioning System (GPS) trajectories of motorcycle taxis in Sigomre area, Siaya County, Kenya. ISPRS Int. J. Geoinf. 2018, 7, 309. [Google Scholar] [CrossRef]
- Gil, J. Building a multimodal urban network model using openstreetmap data for the analysis of sustainable accessibility. In OpenStreetMap in GIScience; Springer: Cham, Switzerland, 2015; pp. 229–251. [Google Scholar]
- May, A.; Parker, C.J.; Taylor, N.; Ross, T. Evaluating a concept design of a crowd-sourced ‘mashup’ providing ease-of-access information for people with limited mobility. Transp. Res. Part C Emerg. Technol. 2014, 49, 103–113. [Google Scholar] [CrossRef]
- Piao, B.; Aihara, K.; Kinoshita, A.; Takasu, A.; Adachi, J. Estimating road surface condition using crowdsourcing. In Information Search, Integration, and Personlization; Kotzinos, D., Laurent, D., Petit, J.M., Spyratos, N., Tanaka, Y., Eds.; Springer: Cham, Switzerland, 2017; pp. 66–81. [Google Scholar]
- Semanjski, I.; Gautama, S. Crowdsourcing mobility insights—Reflection of attitude based segments on high resolution mobility behaviour data. Transp. Res. Part C Emerg. Technol. 2016, 71, 434–446. [Google Scholar] [CrossRef]
- Shin, D.; Aliaga, D.; Tunçer, B.; Arisona, S.M.; Kim, S.; Zünd, D.; Schmitt, G. Urban sensing: Using smartphones for transportation mode classification. Comput. Environ. Urban Syst. 2015, 53, 76–86. [Google Scholar] [CrossRef]
- Gu, W.; Jin, M.; Zhou, Z.; Spanos, C.J.; Zhang, L. Metroeye: Smart tracking your metro trips underground. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (ACM, 2016), Hiroshima, Japan, 28 November–1 December 2016; pp. 84–93. [Google Scholar]
- Park, S.; Bang, Y.; Yu, K. Techniques for updating pedestrian network data including facilities and obstructions information for transportation of vulnerable people. Sensors 2015, 15, 24466–24486. [Google Scholar] [CrossRef] [PubMed]
- Griffin, G.P.; Jiao, J. Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus. J. Transp. Health 2015, 2, 238–247. [Google Scholar] [CrossRef]
- Laatikainen, T.E.; Haybatollahi, M.; Kyttä, M. Environmental, individual and personal goal influences on older adults’ walking in the helsinki metropolitan area. Int. J. Environ. Res. Public Health 2019, 16, 58. [Google Scholar] [CrossRef] [PubMed]
- Kajosaari, A.; Hasanzadeh, K.; Kyttä, M. Residential dissonance and walking for transport. J. Transp. Geogr. 2019, 74, 134–144. [Google Scholar] [CrossRef]
- Giuffrida, N.; Ignaccolo, M.; Inturri, G.; Rofè, Y.; Calabrò, G. Investigating the correlation between transportation social need and accessibility: The case of Catania. Transp. Res. Proc. 2017, 27, 816–823. [Google Scholar] [CrossRef]
- Giuffrida, N.; Inturri, G.; Caprì, S.; Spica, S.; Ignaccolo, M. The impact of a bus rapid transit line on spatial accessibility and transport equity: The case of Catania. Transport Infrastructure and Systems. In Proceedings of the AIIT International Congress on Transport Infrastructure and Systems, TIS 2017, Rome, Italy, 10–12 April 2017; pp. 753–758. [Google Scholar]
- Ignaccolo, M.; Inturri, G.; Giuffrida, N.; Torrisi, V. Public transport accessibility and social exclusion: Making the connections. In Proceedings of the International Conference on Traffic and Transport Engineering (ICTTE), Belgrade, Serbia, 24–25 November 2016. [Google Scholar]
- Inturri, G.; Ignaccolo, M.; Le Pira, M.; Caprì, S.; Giuffrida, N. Influence of accessibility, land use and transport policies on the transport energy dependence of a city. Transp. Res. Proc. 2017, 25, 3273–3285. [Google Scholar] [CrossRef]
- Mirri, S.; Prandi, C.; Salomoni, P.; Callegati, F.; Campi, A. On combining crowdsourcing, sensing and open data for an accessible smart city. In Proceedings of the 8th International Conference on Next Generation Mobile Apps, Services and Technologies, NGMAST 2014, Oxford, UK, 10–12 September 2014; pp. 294–299. [Google Scholar]
- Gatta, V.; Marcucci, E.; Le Pira, M. Smart urban freight planning process: Integrating desk, living lab and modelling approaches in decision-making. Eur. Transp. Res. Rev. 2017, 9, 32. [Google Scholar] [CrossRef]
- Marcucci, E.; Le Pira, M.; Carrocci, C.S.; Gatta, V.; Pieralice, E. Connected shared mobility for passengers and freight: Investigating the potential of crowdshipping in urban areas. In Proceedings of the 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017, Naples, Italy, 26–28 June 2017; pp. 839–843. [Google Scholar]
- Gatta, V.; Marcucci, E.; Nigro, M.; Patella, S.; Serafini, S. Public transport-based crowdshipping for sustainable city logistics: Assessing economic and environmental impacts. Sustainability 2019, 11, 145. [Google Scholar] [CrossRef]
- Kusumo, A.N.L.; Reckien, D.; Verplanke, J. Utilising volunteered geographic information to assess resident’s flood evacuation shelters. Case study: Jakarta. Appl. Geogr. 2017, 88, 174–185. [Google Scholar] [CrossRef]
- Yang, B.; Zhang, Y.; Lu, F. Geometric-based approach for integrating VGI POIs and road networks. Int. J. Geogr. Inf. Sci. 2014, 28, 126–147. [Google Scholar] [CrossRef]
- Sun, Y.; Li, M. Investigation of travel and activity patterns using location-based social network data: A case study of active mobile social media users. ISPRS Int. J. Geoinf. 2015, 4, 1512–1529. [Google Scholar] [CrossRef]
- Sun, Y.; Mobasheri, A. Utilizing crowdsourced data for studies of cycling and air pollution exposure: A case study using strava data. Int. J. Environ. Res. Public Health 2017, 14, 274. [Google Scholar] [CrossRef]
- McArthur, D.P.; Hong, J. Visualising where commuting cyclists travel using crowdsourced data. J. Transp. Geogr. 2019, 74, 233–241. [Google Scholar] [CrossRef]
- Sun, Y.; Moshfeghi, Y.; Liu, Z. Exploiting crowdsourced geographic information and GIS for assessment of air pollution exposure during active travel. J. Transp. Health 2017, 6, 93–104. [Google Scholar] [CrossRef]
- Jestico, B.; Nelson, T.; Winters, M. Mapping ridership using crowdsourced cycling data. J. Transp. Geogr. 2016, 52, 90–97. [Google Scholar] [CrossRef]
- Szell, M. Crowdsourced quantification and visualization of urban mobility space inequality. Urban Plan. 2018, 3, 1–20. [Google Scholar] [CrossRef]
- Sultan, J.; Ben-Haim, G.; Haunert, J.H.; Dalyot, S. Extracting spatial patterns in bicycle routes from crowdsourced data. Trans. GIS 2017, 21, 1321–1340. [Google Scholar] [CrossRef]
- Kunwar, B.; Simini, F.; Johansson, A. Large scale pedestrian evacuation modeling framework using volunteered geographical information. Transp. Res. Proc. 2014, 2, 813–818. [Google Scholar] [CrossRef]
- Novack, T.; Wang, Z.; Zipf, A. A system for generating customized pleasant pedestrian routes based on OpenStreetMap data. Sensors 2018, 18, 3794. [Google Scholar] [CrossRef] [PubMed]
- Kyttä, M. SoftGIS methods in planning evaluation. In Evaluation for Participatory and Sustainable Planning; Hull, A., Ed.; Routledge: Abingdon, UK, 2011. [Google Scholar]
- Kahila, M.; Kyttä, M. SoftGIS method as a bridge builder in collaborative urban planning. In Planning Support Systems: Best Practices and New Methods; Geertman, S., Stillwell, J., Eds.; Springer: Dordrecht, The Netherlands, 2009; pp. 389–411. [Google Scholar]
- Moore, S.A.; Brown, G.; Kobryn, H.; Strickland-Munro, J. Identifying conflict potential in a coastal and marine environment using participatory mapping. J. Environ. Manag. 2017, 197, 706–718. [Google Scholar] [CrossRef] [PubMed]
- Strickland-Munro, J.; Kobryn, H.; Brown, G.; Moore, S.A. Marine spatial planning for the future: Using Public Participation GIS (PPGIS) to inform the human dimension for large marine parks. Mar. Policy 2016, 73, 15–26. [Google Scholar] [CrossRef]
- Brown, G.; Strickland-Munro, J.; Kobryn, H.; Moore, S.A. Stakeholder analysis for marine conservation planning using public participation GIS. Appl. Geogr. 2016, 67, 77–93. [Google Scholar] [CrossRef]
- Ignaccolo, M.; Inturri, G.; García-Melón, M.; Giuffrida, N.; Le Pira, M.; Torrisi, V. Combining analytic hierarchy process (AHP) with role-playing games for stakeholder engagement in complex transport decisions. Transp. Res. Proc. 2017, 27, 500–507. [Google Scholar] [CrossRef]
- Pensa, S.; Masala, E.; Arnone, M.; Rosa, A. Planning local public transport: A visual support to decision-making. Proc. Soc. Behav. Sci. 2014, 111, 596–603. [Google Scholar] [CrossRef]
- Cascetta, E. Teoria e Metodi dell’Ingegneria dei Sistemi di Trasporto; UTET: Torino, Italy, 1998. [Google Scholar]
- Pagliara, F.; Esposito, M.; Troisi, C. Planning and designing transport infrastructures for sustainability: The great project NAPLEST. Period. Polytech. Transp. Eng. 2017, 45, 53–57. [Google Scholar] [CrossRef]
- Verma, R.; Ghosh, S.; Shrivastava, A.; Ganguly, N.; Mitra, B.; Chakraborty, S. Unsupervised annotated city traffic map generation. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2016, San Francisco, CA, USA, 31 October–3 November 2016; p. 59. [Google Scholar]
- Baker, K.; Ooms, K.; Verstockt, S.; Brackman, P.; De Maeyer, P.; Van de Walle, R. Crowdsourcing a cyclist perspective on suggested recreational paths in real-world networks. Cartogr. Geogr. Inf. Sci. 2017, 44, 422–435. [Google Scholar] [CrossRef]
- Tafidis, P.; Teixeira, J.; Bahmankhah, B.; Macedo, E.; Coelho, M.C.; Bandeira, J. Exploring crowdsourcing information to predict traffic-related impacts. In Proceedings of the 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I&CPS Europe, Milan, Italy, 6–9 June 2017; pp. 1–6. [Google Scholar]
- Rohit, V. CrowdMap: Crowdsourcing based city traffic map generation. In Proceedings of the 9th International Conference on Communication Systems and Networks, COMSNETS 2017, Bengaluru, India, 4–8 January 2017; pp. 582–583. [Google Scholar]
- Mozas-Calvache, A.T. Analysis of behaviour of vehicles using VGI data. Int. J. Geogr. Inf. Sci. 2016, 30, 2486–2505. [Google Scholar] [CrossRef]
- Liu, S.; Qu, Q. Dynamic collective routing using crowdsourcing data. Transp. Res. Part B Method. 2016, 93, 450–469. [Google Scholar] [CrossRef]
- Kashiyama, T.; Panga, Y.; Sekimoto, Y. Open PFLOW: Creation and evaluation of an open dataset for typical people mass movement in urban areas. Transp. Res. Part C Emerg. Technol. 2017, 85, 249–267. [Google Scholar] [CrossRef]
- Cairo, O.; Salcedo, J.S.; Gutierrez-Garcia, J.O. Crowdsourcing information for knowledge-based design of routes for unscheduled public transport trips. J. Knowl. Manag. 2015, 19, 626–640. [Google Scholar] [CrossRef]
- Pánek, J.; Benediktsson, K. Emotional mapping and its participatory potential: Opinions about cycling conditions in Reykjavík, Iceland. Cities 2017, 61, 65–73. [Google Scholar] [CrossRef]
- Salonen, M.; Broberg, A.; Kyttä, M.; Toivonen, T. Do suburban residents prefer the fastest or low-carbon travel modes? Combining public participation GIS and multimodal travel time analysis for daily mobility research. Appl. Geogr. 2014, 53, 438–448. [Google Scholar] [CrossRef]
- Czepkiewicz, M.; Ottelin, J.; Ala-Mantila, S.; Heinonen, J.; Hasanzadeh, K.; Kyttä, M. Urban structural and socioeconomic effects on local, national and international travel patterns and greenhouse gas emissions of young adults. J. Transp. Geogr. 2018, 68, 130–141. [Google Scholar] [CrossRef]
- Laatikainen, T.E.; Piiroinen, R.; Lehtinen, E.; Kyttä, M. PPGIS approach for defining multimodal travel thresholds: Accessibility of popular recreation environments by the water. Appl. Geogr. 2017, 79, 93–102. [Google Scholar] [CrossRef]
- Galpern, P.; Ladle, A.; Uribe, F.A.; Sandalack, B.; Doyle-Baker, P. Assessing urban connectivity using volunteered mobile phone GPS locations. Appl. Geogr. 2018, 93, 37–46. [Google Scholar] [CrossRef]
- Lu, Y.; Misra, A.; Wu, H. Smartphone sensing meets transport data: A collaborative framework for transportation service analytics. IEEE Trans. Mob. Comput. 2018, 17, 945–960. [Google Scholar] [CrossRef]
- May, A.; Ross, T. The design of civic technology: Factors that influence public participation and impact. Ergonomics 2018, 61, 214–225. [Google Scholar] [CrossRef]
- Sandheep, S.; Harry, J.; Harikumar, A.; Vinitha Panicker, J.V. BusTimer: An android based application for generating bus schedules using crowdsourcing. In Proceedings of the 2017 International Conference on Technological Advancements in Power and Energy, TAP Energy, Coimbatore, India, 21–23 December 2017; pp. 1–6. [Google Scholar]
- Wu, F.-J.; Lim, H.B. UrbanMobilitySense: A user-centric participatory sensing system for transportation activity surveys. IEEE Sens. J. 2014, 14, 4165–4174. [Google Scholar] [CrossRef]
- Bucher, D.; Mangili, F.; Cellina, F.; Bonesana, C.; Jonietz, D.; Raubal, M. From location tracking to personalized eco-feedback: A framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. Travel Behav. Soc. 2019, 14, 43–56. [Google Scholar] [CrossRef]
- Baloian, N.; Frez, J.; Pino, J.A.; Zurita, G. Efficient planning of urban public transportation networks. In International Conference on Ubiquitous Computing and Ambient Intelligence; Springer: Cham, Switzerland, 2015; pp. 439–448. [Google Scholar]
- Ferster, C.J.; Nelson, T.; Winters, M.; Laberee, K. Geographic age and gender representation in volunteered cycling safety data: A case study of BikeMaps.org. Appl. Geogr. 2017, 88, 144–150. [Google Scholar] [CrossRef]
- Sun, Y.; Du, Y.; Wang, Y.; Zhuang, L. Examining associations of environmental characteristics with recreational cycling behaviour by street-level Strava data. Int. J. Environ. Res. Public Health 2017, 14, 644. [Google Scholar] [CrossRef]
- Sobreiro Santos, G.; de Arantes Gomes, R.; dos Santos, E.A. PPGIS as an urban planning tool around airports. J. Air Transp.Manag. 2018, 69, 269–278. [Google Scholar] [CrossRef]
- Li, M.Z.; Suh, D.Y.; Ryerson, M.S. Visualizing aviation impacts: Modeling current and future flight trajectories with publicly available flight data. Transp. Res. Part D Transp. Environ. 2018, 63, 769–785. [Google Scholar] [CrossRef]
- Dienstl, B.; Scholz, J. A concept for smart transportation user-feedback utilizing volunteered geoinformation approaches. Adv. Intell. Syst. Comput. 2019, 879. [Google Scholar] [CrossRef]
- Cao, X.; Feiwen, L.; Huiling, C. Circuity characteristics of urban travel based on GPS data: A case study of Guangzhou. Sustainability 2017, 9, 2156. [Google Scholar]
- Teymurian, F.; Alesheikh, A.A.; Alimohammadi, A.; Sadeghi-Niaraki, A. VGI based urban public transport. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2013, XL-1/W3, 425–430. [Google Scholar] [CrossRef]
- Liu, K.; Siu, K.W.M.; Gong, X.Y.; Gao, Y.; Lu, D. Where do networks really work? The effects of the Shenzhen greenway network on supporting physical activities. Lands. Urban Plan. 2016, 152, 49–58. [Google Scholar] [CrossRef]
- Vajjhala, S.P.; Walker, W.M. Roads to participatory planning: integrating cognitive mapping and GIS for transport prioritization in rural Lesotho. J. Maps 2010, 6, 488–504. [Google Scholar] [CrossRef]
- Bordogna, G.; Capelli, S.; Ciriello, D.E.; Psaila, G. A cross-analysis framework for multi-source volunteered, crowdsourced, and authoritative geographic information: The case study of volunteered personal traces analysis against transport network data. Geosp. Inf. Sci. 2018, 21, 257–271. [Google Scholar] [CrossRef]
- Guerlain, C.; Cortina, S.; Renault, S. Towards a collaborative geographical information system to support collective decision making for urban logistics initiative. Transp. Res. Proc. 2016, 12, 634–643. [Google Scholar] [CrossRef]
- Bakillah, M.; Liang, S.H.L.; Zipf, A. Toward coupling sensor data and volunteered geographic information (VGI) with agent-based transport simulation in the context of smart cities. In Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Redondo Beach, CA, USA, 6–9 November 2012. [Google Scholar]
- Singla, A.; Santoni, M.; Bartók, G.; Mukerji, P.; Meenen, M.; Krause, A. Incentivizing users for balancing bike sharing systems. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, TX, USA, 25–30 January 2015. [Google Scholar]
- Tomaras, D. Crowdsourcing techniques for smart urban mobility. In Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops, 2018), Athens, Greece, 19–23 March 2018; pp. 460–461. [Google Scholar]
- Gavalas, D.; Giannakopoulou, K.; Kasapakis, V.; Kehagias, D.; Konstantopoulos, C.; Kontogiannis, S.; Kypriadis, D.; Pantziou, G.; Paraskevopoulos, A.; Zaroliagis, C. Renewable mobility in smart cities: Cloud-based services. In Proceedings of the 2018 IEEE Symposium on Computers and Communications (ISCC, 2018), Natal, Brazil, 25–28 June 2018; pp. 01280–01285. [Google Scholar]
- Attard, M.; Haklay, M.; Capineri, C. The potential of volunteered geographic information (VGI) in future transport systems. Urban Plan. 2016, 1, 6–19. [Google Scholar] [CrossRef]
- Kessler, F. Volunteered geographic information: A bicycling enthusiast perspective. Cartogr. Geogr. Inf. Sci. 2011, 38, 258–268. [Google Scholar] [CrossRef]
- Browne, D.; Ryan, L. Comparative analysis of evaluation techniques for transport policies. Environ. Impact Assess. Rev. 2011, 31, 226–233. [Google Scholar] [CrossRef]
- Le Pira, M.; Inturri, G.; Ignaccolo, M.; Pluchino, A. Modelling consensus building in Delphi practices for participated transport planning. Transp. Res. Proc. 2017, 25, 3729–3739. [Google Scholar] [CrossRef]
- Macharis, C.; De Witte, A.; Ampe, J. The multi-actor, multi-criteria analysis methodology (MAMCA) for the evaluation of transport projects: Theory and practice. J. Adv. Transp. 2009, 43, 183–202. [Google Scholar] [CrossRef]
- Malczewski, J. GIS-based multicriteria decision analysis: A survey of the literature. Int. J. Geogr. Inf. Sci. 2006, 20, 703–726. [Google Scholar] [CrossRef]
- Malczewski, J. GIS and Multicriteria Decision Analysis; Wiley: New York, NY, USA, 1999. [Google Scholar]
- Giuffrida, N. A Public Participatory GIS and Multi Criteria Decision Analysis framework for the Evaluation of Transport Scenarios. Ph.D. Thesis, University of Catania, Catania, Italy, 2018. Available online: http://hdl.handle.net/10761/3897 (accessed on 23 April 2019).
- Gatta, V.; Marcucci, E.; Delle Site, P.; Le Pira, M.; Carrocci, C.S. Planning with stakeholders: Analysing alternative off-hour delivery solutions via an interactive multi-criteria approach. Res. Transp. Econ. 2019. [Google Scholar] [CrossRef]
- Ishizaka, A.; Nemery, P. Multi-Criteria Decision Analysis. Methods and Software; Wiley: Hoboken, NJ, USA, 2013. [Google Scholar]
- Fonte, C.C.; Antoniou, V.; Bastin, L.; Estima, J.; Arsanjani, J.J.; Bayas, J.-C.L.; See, L.; Vatseva, R. Assessing VGI Data Quality. In Mapping and the Citizen Sensor; Foody, G., See, L., Fritz, S., Mooney, P., Olteanu-Raimond, A.-M., Fonte, C.C., Antoniou, V., Eds.; Ubiquity Press: London, UK, 2017; pp. 137–163. [Google Scholar] [CrossRef]
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Giuffrida, N.; Le Pira, M.; Inturri, G.; Ignaccolo, M. Mapping with Stakeholders: An Overview of Public Participatory GIS and VGI in Transport Decision-Making. ISPRS Int. J. Geo-Inf. 2019, 8, 198. https://doi.org/10.3390/ijgi8040198
Giuffrida N, Le Pira M, Inturri G, Ignaccolo M. Mapping with Stakeholders: An Overview of Public Participatory GIS and VGI in Transport Decision-Making. ISPRS International Journal of Geo-Information. 2019; 8(4):198. https://doi.org/10.3390/ijgi8040198
Chicago/Turabian StyleGiuffrida, Nadia, Michela Le Pira, Giuseppe Inturri, and Matteo Ignaccolo. 2019. "Mapping with Stakeholders: An Overview of Public Participatory GIS and VGI in Transport Decision-Making" ISPRS International Journal of Geo-Information 8, no. 4: 198. https://doi.org/10.3390/ijgi8040198
APA StyleGiuffrida, N., Le Pira, M., Inturri, G., & Ignaccolo, M. (2019). Mapping with Stakeholders: An Overview of Public Participatory GIS and VGI in Transport Decision-Making. ISPRS International Journal of Geo-Information, 8(4), 198. https://doi.org/10.3390/ijgi8040198