Collective Information Infrastructures During Disaster Response
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Presentation by Robert Soden of University of Colorado, Boulder on joint Crisis Informatics workshop organized by Kathmandu Living Labs, Harvard Humanitarian Initiative and University of Colorado Boulder to draw lessons from Nepal Earthquake 2015.
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Collective Information Infrastructures During Disaster Response
- 1. Collective Information Infrastructures During Disaster Response: Initial Findings from the 2015 Nepal Earthquake Robert Soden PhD Student, Computer Science University of Colorado, Boulder
- 2. Research Question What are the characteristics of the information infrastructure that supported the work of GIS and mapping teams during the Nepal earthquake response? In particular, what has been the impact of community mapping and open data projects?
- 3. Information Infrastructure the people, processes, and tools that support the creation, maintenance, and use of information networks, protocols, standards, formal and informal social arrangements when they function they are invisible
- 4. Methodology • 40 interviews with GIS and information management officers from government, international organizations, and Nepali technical organizations • Trying to develop a detailed understanding of how these people did their work, what maps did they produce and why, what data did they use and why, how did they distribute their products and how were they used
- 5. Findings 1. OpenStreetMap 2. Data Quality 3. Feedback Loops 4. The role of Kathmandu Living Labs
- 14. Data quality
- 18. OSM Volunteers GIS Team Map Users Feedback Loops
- 20. Information systems related to disasters are collective efforts
- 21. Thanks! Robert Soden University of Colorado, Boulder robert.soden@colorado.edu
Editor's Notes
- Information infrastructure is a funny word but I think it evokes a few things that are useful for our talk today. First, when we are talking about infomration and mapping in disaster response we aren’t talking about isolated datasets just sitting on one person’s laptop. We’re instead talking about networks of people, social and technical processes, hardware and software that allow for collaborative efforts to create, share, update, and communicate data. Second infrastructure is something that we need to invest in the creation and maintenance of and that we need to think very carefully about the design.
- Ok so I just have a few minutes to present so I want to focus my remarks
- OpenStreetMap was the default basemap for the majority of the GIS teams This is true of both international organizations, many of whom have come to expect it, and government and local organization What’s more interesting though is the incredible diversity of organizations who used it, the things they used it for, and the ways in which it was presented. Detailed mapping of an area of 10,000km2 in 4 days, including coverage of road networks, hiking trails, built-up areas, building footprints, river crossings and temporary relief camps Quadrupled road mileage and added 30% more buildings in 48 hours Identified 15 priority areas, 8 of which have been completed and validated Attracted over 2,000 volunteer contributors from around the world, 1/3 of whom are new mappers Made maps available on the web as half hourly data exports, print maps, and offline maps for Android
- USAID team with a paper basemap of gorkha
- Search and rescue map by mapaction. Here OSM is used as a basemap and they add operational data on top of it.
- PDF Map of IDP camps by the Red Cross. They have a complicated workflow by which they extract raw data from OSM and bring into their own open source GIS tools where they have a number of map templates set up
- PDF Map published on the webiste of the national geographic information infrastructure project (ngiip) of road networks in lalitpur using OSM
- Interactive ebmap produced by ICIMOD in support of the Ministry of Home Affairs which uses OSM as a basemap
- Logistics map made by WFP
- Mobile App developed by Kathmandu University and ICIMOD
- Sometimes GIS people obsess over cartographic scale, precision, and positional accuracy when talking about GIS data. This is especially the case when it comes to data from crowdsourcing or volunteers. When talking about OpenStreetMap the first question that people always used to ask me was about data quality, how can we ensure it we have accurate data? A lot of the initial academic research into OSM, carried out by Muki Haklay at University College London going back as far as 2007 was about OSM data quality Interestingly this has not been the case here in Nepal. When interviewing GIS teams about which data source they used and why, data quality was far from the most important consideration. If people did have concerns about it, they would usually say that it was sufficient or their purposes or that OSM was the best available. Far more important for these individiauls when choosing which data the would use for their maps were considerations such as access – OSM data is open whereas many other datasets were not, previous experience using OSM, or personal recommendations
- Sometimes GIS people obsess over cartographic scale, precision, and positional accuracy when talking about GIS data. This is especially the case when it comes to data from crowdsourcing or volunteers. When talking about OpenStreetMap the first question that people always used to ask me was about data quality, how can we ensure it we have accurate data? A lot of the initial academic research into OSM, carried out by Muki Haklay at University College London going back as far as 2007 was about OSM data quality Interestingly this has not been the case here in Nepal. When interviewing GIS teams about which data source they used and why, data quality was far from the most important consideration. If people did have concerns about it, they would usually say that it was sufficient or their purposes or that OSM was the best available. Far more important for these individiauls when choosing which data the would use for their maps were considerations such as access – OSM data is open whereas many other datasets were not, previous experience using OSM, or personal recommendations from trusted individuals
- Sometimes GIS people obsess over cartographic scale, precision, and positional accuracy when talking about GIS data. This is especially the case when it comes to data from crowdsourcing or volunteers. When talking about OpenStreetMap the first question that people always used to ask me was about data quality, how can we ensure it we have accurate data? A lot of the initial academic research into OSM, carried out by Muki Haklay at University College London going back as far as 2007 was about OSM data quality Interestingly this has not been the case here in Nepal. When interviewing GIS teams about which data source they used and why, data quality was far from the most important consideration. If people did have concerns about it, they would usually say that it was sufficient or their purposes or that OSM was the best available. Far more important for these individiauls when choosing which data the would use for their maps were considerations such as access – OSM data is open whereas many other datasets were not, previous experience using OSM, or personal recommendations from trusted individuals
- Sometimes GIS people obsess over cartographic scale, precision, and positional accuracy when talking about GIS data. This is especially the case when it comes to data from crowdsourcing or volunteers. When talking about OpenStreetMap the first question that people always used to ask me was about data quality, how can we ensure it we have accurate data? A lot of the initial academic research into OSM, carried out by Muki Haklay at University College London going back as far as 2007 was about OSM data quality Interestingly this has not been the case here in Nepal. When interviewing GIS teams about which data source they used and why, data quality was far from the most important consideration. If people did have concerns about it, they would usually say that it was sufficient or their purposes or that OSM was the best available. Far more important for these individiauls when choosing which data the would use for their maps were considerations such as access – OSM data is open whereas many other datasets were not, previous experience using OSM, or personal recommendations from trusted individuals
- KLL played an important role both before and during the quake Creation of data Outreach and Community Building Interface between local responders and the international OSM community On demand map production KLL not the only one
- Information systems related to disasters are necessarily collective resources - there are many different users and uses involved No one actor has all of the information, everyone needs information that other organizations create or manage. Yet our information infrastructures don't always accommodate this The design of information infrastructure can help communities manage collective information resource successfully, or contribute to inefficiences and information failures, and in the case of disaster response, the failure to deliver lifesaving humanitarian aid in effective ways. We saw during the Nepal Earthquake response and the example of OpenStreetMap that investment in information infrastructures that have openness, accessibility, and collaboration as components is a successful strategy. What we need to now as a community is to understand in more detail how to design them and continue to grow this effort in Nepal. For that reason I am very happy to be here and participate in this workshop with you all today