Abstract
Community asset mapping is an essential step in public health practice for identifying community strengths, needs, and urban health intervention strategies. Community-based Volunteered Geographic Information (VGI) could facilitate customized asset mapping to link free and accessible technologies with community needs in a mutually shared, knowledge-producing process. To address this issue, we demonstrate a participatory asset mapping infrastructure developed with a Chicago community using VGI concepts, participatory design principles, and geospatial Software as a Service (SaaS) using a suite of free and/or open tools. Participatory mapping infrastructures using decentralized system architecture can link data and mapping services, transforming siloed datasets to integrated systems managed and shared across multiple organizations. The final asset mapping infrastructure includes a flexible and cloud-based data management system, an interactive web map, and community asset data stream. By allowing for a dynamic, reproducible, adaptive, and participatory asset mapping system, health systems infrastructures can further support community health improvement frameworks by facilitating shared data and decision support implementations across health partners. Such “community-engaged VGI” is essential in integrating previously siloed data systems and facilitating means of collaboration with health systems in urban health research and practice.
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References
Ananthakrishnan, R., Chard, K., Foster, I., & Tuecke, S. (2015). Globus platform-as-a-service for collaborative science applications. Concurrency and Computation: Practice and Experience, 27(2), 290–305.
Bittner, C., Glasze, G., & Turk, C. (2013). Tracing contingencies: Analyzing the political in assemblages of web 2.0 cartographies. GeoJournal, 78(6), 935–948.
Bote-Lorenzo, M. L., Dimitriadis, Y. A., & G mez-Sánchez, E. (2004). Grid characteristics and uses: A grid definition. In Grid computing (pp. 291–298). Berlin/Heidelberg: Springer.
Boulos, M. N. K., Resch, B., Crowley, D., Breslin, J., Sohn, G., Burtner, R., et al. (2011). Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: Trends, OGC standards and application examples. International Journal of Health, 10, 67.
Brandusescu, A., & Sieber, R. E. (2018). The spatial knowledge politics (SKP) of crisis mapping for community development. GeoJournal, 1, 1–16.
Brown, G., Rhodes, J., & Dade, M. (2018). An evaluation of participatory mapping methods to assess urban park benefits. Landscape and Urban Planning, 178, 18–31.
Burns, R. (2015). Rethinking big data in digital humanitarianism: Practices, epistemologies, and social relations. GeoJournal, 80(4), 477–490.
Center for Disease and Control (CDC). (2015). Community health improvement navigator.
Chicago Department of Public Health. (2017). Chicago health atlas resources. https://www.chicagohealthatlas.org/resources.
Cinnamon, J., & Schuurman, N. (2013). Confronting the data-divide in a time of spatial turns and volunteered geographic information. GeoJournal, 78(4), 657–674.
Cochrane, L., & Corbett, J. (2018). Participatory mapping. Handbook of communication for development and social change, 1–9.
Cochrane, L., Corbett, J., Evans, M., & Gill, M. (2017). Searching for social justice in GIScience publications. Cartography and Geographic Information Science, 44(6), 507–520.
Coetzee, S., & Wolff-Piggott, B. (2015). A review of sdi literature: Searching for signs of inverse infrastructures. In Cartography-maps connecting the world (pp. 113–127). Cham: Springer.
Eder, D. (2015). Searchable map template with Google fusion tables. https://github.com/derekeder/FusionTable-Map-Template.
Egyedi, T. M., & Mehos, D. C. (Eds.). (2012). Inverse Infrastructures: Disrupting networks from below. Edward Elgar Publishing.
Egyedi, T. M., Vrancken, J. L., & Ubacht, J. (2007). Inverse infrastructures: Coordination in self-organizing systems. In Standardization and innovation in information technology, 2007. SIIT 2007. 5th international conference on (pp. 23–36). IEEE.
Elwood, S. (2006). Critical issues in participatory GIS: Deconstructions, reconstructions, and new research directions. Transactions in GIS, 10(5), 693–708.
Elwood, S. (2008). Volunteered geographic information: Future research directions motivated by critical, participatory, and feminist GIS. GeoJournal, 72(3–4), 173–183.
Elwood, S. (2008b). Volunteered geographic information: key questions, concepts and methods to guide emerging research and practice. GeoJournal, 72(3-4), 133–135.
Elwood, S. (2009). Multiple representations, significations, and epistemologies in community-based GIS. In Qualitative GIS: A mixed methods approach (pp. 57–74).
Elwood, S., Goodchild, M. F., & Sui, D. Z. (2012). Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the Association of American Geographers, 102(3), 571–590.
English, P. B., Richardson, M. J., & Garzón-Galvis, C. (2018). From crowdsourcing to extreme citizen science: Participatory research for environmental health. Annual Review of Public Health, 39, 335–350.
Fast, V., & Rinner, C. (2018). Toward a participatory VGI methodology: Crowdsourcing information on regional food assets. International Journal of Geographical Information Science, 1, 1–16.
Foster, I., & Kesselman, C. (1999). “The globus toolkit.” The grid: blueprint for a new computing infrastructure: 259-278. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA. ISBN:1-55860-475-8.
Foster, I., Kesselman, C., & Tuecke, S. (2001). The anatomy of the grid: Enabling scalable virtual organizations. The International Journal of High Performance Computing Applications, 15(3), 200–222.
Gao, S., Li, L., Li, W., Janowicz, K., & Zhang, Y. (2017). Constructing gazetteers from volunteered Big Geo-Data based on Hadoop. Computers, Environment and Urban Systems, 61, 172–186.
Ghose, R., & Welcenbach, T. (2018). “Power to the people”: Contesting urban poverty and power inequities through open GIS. The Canadian Geographer/Le Géographe canadien, 62(1), 67–80.
Glasze, G., & Perkins, C. (2015). Social and political dimensions of the OpenStreetMap project: Towards a critical geographical research agenda. In OpenStreetMap in GIScience (pp. 143–166). Cham: Springer.
Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69(4), 211–221.
Goranson, C., Thihalolipavan, S., & di Tada, N. (2013). VGI and public health: Possibilities and pitfalls. In Crowdsourcing geographic knowledge (pp. 329–340). Dordrecht: Springer.
Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The ‘big data’ revolution in healthcare. McKinsey Quarterly, 2, 3.
Hardy, D., Frew, J., & Goodchild, M. F. (2012). Volunteered geographic information production as a spatial process. International Journal of Geographical Information Science, 26(7), 1191–1212.
Healthy People. Secretary’s Advisory Committee on Health Promotion and Disease Prevention Objectives for 2020. Healthy People 2020: An opportunity to address the societal determinants of health in the United States. http://www.healthypeople.gov/2020/topicsobjectives2020/overview.aspx?topicid=39.
Hecht, B. J., & Gergle, D. (2010, February). On the localness of user-generated content. In Proceedings of the 2010 ACM conference on Computer supported cooperative work (pp. 229–232). ACM.
Hobona, G., Jackson, M., & Anand, S. (2012). Implementing Geospatial Web Services for Cloud Computing. In I. Management Association (Ed.), Grid and cloud computing: Concepts, methodologies, tools and applications (pp. 615–636). Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-4666-0879-5.ch305
Kang, S. Y., & Lee, Y. H. (2014). The implementation of geo-cloud SaaS system for supporting the civil engineering design using BRMS open software. 2014 fifth international conference on computing for geospatial research and application (pp. 49–50).
Kerka, S. (2003). Community asset mapping. Trends and Issues Alert, (47). ERIC Clearinghouse on Adult, Career, and Vocational Education, Columbus, OH.
Kolak, M., & Stepanoe, M. (2016). “HumboldtResources: Alpha” Zenodo. https://doi.org/10.5281/zenodo.44691.2016.
Korpilo, S., Virtanen, T., Saukkonen, T., & Lehvävirta, S. (2018). More than A to B: Understanding and managing visitor spatial behaviour in urban forests using public participation GIS. Journal of Environmental Management, 207, 124–133.
Kramer, S., Amos, T., Lazarus, S., & Seedat, M. (2012). The philosophical assumptions, utility and challenges of asset mapping approaches to community engagement. Journal of Psychology in Africa, 22(4), 537–544.
Kretzmann, J. P., & McKnight, J. (1993). Building communities from the inside out (pp. 2–10). Evanston: Center for Urban Affairs and Policy Research, Neighborhood Innovations Network.
Lightfoot, E., McCleary, J. S., & Lum, T. (2014). Asset mapping as a research tool for community-based participatory research in social work. Social Work Research, 38(1), 59–64.
Mandarano, L., Meenar, M., & Steins, C. (2010). Building social capital in the digital age of civic engagement. Journal of Planning Literature, 25(2), 123–135.
Meier, P. (2011). Verifying crowdsourced social media reports for live crisis mapping: An introduction to information forensics. iRevolution blog.
Pramono, A. H., Natalia, I., & Janting, Y. (2006). Ten years after: Counter-mapping and the Dayak lands in West Kalimantan, Indonesia. Digital Library of the Commons.
Qazi, N., Smyth, D., & McCarthy, T. (2013). Towards a GIS-based decision support system on the Amazon cloud for the modelling of domestic wastewater treatment solutions in Wexford, Ireland. 2013 Uksim 15Th international conference on computer modelling and simulation, 236–240.
Raymond, C. M., Gottwald, S., Kuoppa, J., & Kyttae, M. (2016). Integrating multiple elements of environmental justice into urban blue space planning using public participation geographic information systems. Landscape and Urban Planning, 153, 198–208.
Sadler, R. C. (2016). Integrating expert knowledge in a GIS to optimize siting decisions for small-scale healthy food retail interventions. International Journal of Health Geographics, 15(1), 19.
Sieber, R. E., Robinson, P. J., Johnson, P. A., & Corbett, J. M. (2016). Doing public participation on the geospatial web. Annals of the American Association of Geographers, 106(5), 1030–1046.
Solís, P., McCusker, B., Menkiti, N., Cowan, N., & Blevins, C. (2018). Engaging global youth in participatory spatial data creation for the UN sustainable development goals: The case of open mapping for malaria prevention. Applied Geography, 98, 143–155.
Spinuzzi, C. (2005). The methodology of participatory design. Technical Communication, 52(2), 163–174.
Stensgaard, A. S., Saarnak, C. F. L., Utzinger, J., Vounatsou, P., Simoonga, C., Mushinge, G., et al. (2009). Virtual globes and geospatial health: The potential of new tools in the management and control of vector-borne diseases. Geospatial Health, 3(2), 127–114.
Tsou, M. H., & Buttenfield, B. P. (2002). A dynamic architecture for distributing geographic information services. Transactions in GIS, 6(4), 355–381.
Vaquero, L. M., Rodero-Merino, L., Caceres, J., & Lindner, M. (2008). A break in the clouds: Towards a cloud definition. ACM SIGCOMM Computer Communication Review, 39(1), 50–55.
Vree, W. G. (2003). Internet en Rijkswaterstaat: een ICT-infrastructuur langs water en wegen.
Wang, L., Tao, J., Kunze, M., Castellanos, A. C., Kramer, D., & Karl, W. (2008, September). Scientific cloud computing: Early definition and experience. In High performance computing and communications, 2008. HPCC’08. 10th IEEE international conference on (pp. 825–830). IEEE.
West Humboldt Park Development Council, 2013.
Yang, C., Li, W., Xie, J., & Zhou, B. (2008). Distributed geospatial information processing: Sharing distributed geospatial resources to support Digital Earth. International Journal of Digital Earth, 1(3), 259–278.
Yu, J., Wu, J., & Sarwat, M. (2015). Geospark: A cluster computing framework for processing large-scale spatial data. In Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems (p. 70).
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M. J., Shenker, S., & Stoica, I. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In Proceedings of the 9th USENIX conference on networked systems design and implementation (Vol. 2).
Zaharia, M., Franklin, M., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I., et al. (2016). Apache spark. Communications of the ACM, 59(11), 56–65.
Zhan, J., Sha, Y., & Yan, J. (2012). Design and implementation of logistics vehicle monitoring system based on the SaaS model. 2012 fifth international conference on business intelligence and financial engineering (pp. 524–526).
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Kolak, M. et al. (2020). Extending Volunteered Geographic Information (VGI) with Geospatial Software as a Service: Participatory Asset Mapping Infrastructures for Urban Health. In: Lu, Y., Delmelle, E. (eds) Geospatial Technologies for Urban Health. Global Perspectives on Health Geography. Springer, Cham. https://doi.org/10.1007/978-3-030-19573-1_11
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