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Geospatial Data Poverty in Developing Countries and Community-Based Data Collection Alternatives

Annaka Scheeres

Environmental Studies Honors Thesis Calvin College Department of Geology, Geography, and Environmental Studies

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Introduction Personal reflection from experience, whether local, national, or international in scope, often provides the impetus for research passion and direction. In January 2016, I traveled to Ethiopia for a joint geography and engineering class investigating the technical and social aspects of clean water. As part of the trip, our small group stayed in Tach Gayint, a rural woreda (or district) in the northern Ethiopian region of Amhara, for two weeks and worked with Food for the Hungry (FH).

Figure 1. Location of Tach Gayint, a woreda in Ethiopia’s Amhara region (Source: Google Maps)

Figure 2. Map of Ethiopia identifying location of Tach Gayint (Source: Google Maps)

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This non-profit organization focuses on water and food security in drought-prone northern Ethiopia, which has been plagued by severe famines over the past decade. Ethiopia’s geographic location in west Africa renders it particularly vulnerable to the ravaging impacts of climate change; moreover, because Ethiopia’s economy is based primarily on agricultural exports, farmers must learn to adapt their techniques to the rapidly changing landscape. In a country where climate change is an embodied reality, access to high-quality data about agricultural land and human health assumes elevated significance. While in Tach Gayint, I had the privilege of providing the FH staff with technical geographic information systems (GIS) support to visualize and manage water irrigation project data. This experience revealed the close connection between high-quality data accessibility and human flourishing. Poor data collection, standardization, and management methods perpetuate poverty, and in many ways, access to high-quality data becomes a matter life or death in developing countries. One of the GIS experts at Food for the Hungry told stories about how poor data collection procedures not only render Ethiopia’s census relatively ineffective, but can also endanger vulnerable human lives. Due to a variety of administrative and personnel decisions in government, each year the census boundaries, such as those equivalent to U.S. Census designated boundaries like tracts, block groups, and blocks, are redrawn, making it impossible to conduct long-term temporal analysis for key demographic variables, such as the prevalence of problematic childhood diseases. Working in a context where geospatial data is scarce highlighted the need for community involvement in data collection and emphasized the significant power of data accessibility. Defining Geospatial Data Poverty Data poverty ensues when countries lack a centralized national data infrastructure and, therefore, struggle to effectively collect, collate, analyze, and release data. Data poverty has significant ramifications for monitoring poverty, health inequality, and other social variables as the United Nations identified in a press release from 2014, “… data deprivation can lead to the denial of basic rights, and for the planet, to continued environmental degradation” (Umar et al. 2015). In September 2015, the United Nations released an updated set of Sustainable Development Goals that define a plan to eradicate poverty, promote sustainability, and foster peace by 2030. Goal 17 outlines a variety of different partnerships between governments, the private sector, and civil society that are necessary for promoting a successful sustainability agenda. One of the components of this goal explicitly addresses data monitoring and accountability for developing countries:

“By 2020, enhance capacity-building support to developing countries, including for least developed countries and small island developing States, to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts. By 2030, build on existing initiatives to develop measurements of progress on sustainable development that complement gross domestic product, and support statistical capacity-building in developing countries” (Umar et al. 2015).

This goal acknowledges that increased investment in data infrastructure (specifically geospatial data infrastructure) is necessary for more effective data collection and collation in developing countries; however, it fails to layout a feasible plan for achieving more effective data collection and monitoring procedures. Additionally, even though the governments of many developing

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countries have highly restrictive policies regarding data accessibility and distribution, this goal does not explicitly call for greater data accessibility or open data infrastructure, which is essential for government accountability and productive scholarly research. Geospatial data encompasses a broad range of disciplines, including demography, epidemiology, economics, geography, and ecology. Therefore, geospatial data poverty specifically refers to the lack of accessible, high-quality data regarding spatial phenomena. A case study regarding the Haiti earthquake and subsequent Cholera outbreak in 2010 highlights the detrimental impacts of geospatial data poverty. Because location is “the founding myth of epidemiology,” the absence of accurate maps makes effective treatment strategies impossible (Zook et al. 2010). A lack of basic but accurate maps prevented aid workers from rapidly reaching vulnerable, affected communities; consequently, many cities and road networks around the world remain unmapped or are mapped using crude, inaccurate methods. Given the broad and specific challenges of geospatial data poverty in developing countries, this paper explores possible alternatives to enhance spatial data infrastructure development. The primary approach explored through this research is how community-based mapping data projects in developing countries can provide foundational momentum towards a grass-roots approach to geovisualization. I argue that if developing countries are stymied from the top-down towards building greater geospatial infrastructure due to security concerns, lack of trust, or totalitarian governance, populations in these circumstances can use readily-available mapping outlets to advance local community advantage. Moreover, communities can use these initiatives to orient government decisions towards establishing a more open, structured geospatial data infrastructure. Contextualizing Geospatial Data Poverty According to researchers Sarah Williams, Elizabeth Marcello, and Jacqueline Klopp, “geographic data are highly political … [because] maps are powerful tools that can serve specific interests and represent different ways of conceiving, articulating and structuring the human world” (2014). Maps both produce and create power, which gives the mapmaker significant control in communicating geospatial data and information. Depending on the way information is conveyed, the mapmaker can manipulate a community into understanding differing spatial narratives that are either empowering or disempowering (Williams et al. 2014). In many developing countries, the government has complete ownership of geospatial data and tightly controls its dissemination because having access to data is conflated with power. In geographic literature, access is commonly defined by “context, connectivity, capabilities, and content” (Laituri 2003). When one of these components is missing from the research process, potential users do not have full access to the GIS data in question. For example, the most powerful GIS tools that maximize spatial analysis capabilities are expensive and require technical training to use correctly, which limits who will ultimately benefit most from these tools. Even if researchers or nonprofit organizations in developed countries have access to relevant datasets, they will not have full access until they can also access the necessary software and hardware. Researchers and nonprofit organizations in developing countries have more restricted access than their counterparts in developed countries because they often cannot access high-quality datasets, powerful GIS software, functional hardware, or well-functioning spatial

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data infrastructures (SDIs). These collaborative data-sharing networks “allow for sharing and access of data across different organizational and government entities” (Williams et al. 2014). Establishing a spatial data infrastructure (SDI) is difficult in a developing country context because of “technological barriers, funding issues, and government and political capacity to build these structures” (Nkambwe 2001). Governments of developing countries are often highly restrictive of geographic data because it demarcates land ownership, property boundaries, and other politically contentious realities (Bishop et al. 2000). Moreover, the governments of many developing countries do not have the means to establish a sophisticated national SDI for a variety of interrelated reasons. Developing countries often lack standardized regulations for land planning and management, which results in disorganized land records; this lack of well-defined geographic boundaries precludes developing successful SDIs. Technical GIS expertise and access to geospatial data is often constrained to government entities, meaning that the average citizen cannot effectively access or analyze geospatial data. The commodification of spatial data also impedes SDI development in developing countries because “spatial data become subject to market forces and hence available only to those who can afford them” (Williams et al. 2014). This produces uneven global and local data accessibility landscapes and discourages the governments of developing countries from openly disseminating data because it is such a valuable commodity. Open access data destabilizes the power hierarchy associated with government-owned data because individual researchers or nonprofit organization can easily access this information. When local communities develop data, they are helping to subvert the relative power that the traditional producers of GIS data possess (Williams et al. 2014). Defining Data Collection Alternatives New digital tools are becoming increasingly important in developing disease control and eradication programs. GPS-enabled smartphones can efficiently and accurately gather field data about disease locations and collect patient surveys, and dashboards, maps, and computer simulations are crucial for making well-informed decisions (Chabot-Couture et al. 2015). Although coordinating these tools on a large scale can be difficult, these tools are helping monitor, control, and distribute medication for communicable diseases. For example, a polio eradication campaign in Nigeria has greatly benefited from maps created using high-resolution satellite imagery and field GPS data collection; these maps ensured that all settlements were considered in the planning process and allowed for more equal vaccine distribution. Additionally, as the vaccination teams headed from village to village, their GPS-enable smartphone collected time-stamped positions that accurately tracked geographic coverage (Chabot-Couture et al. 2015). Using geographic data, as well as qualitative data of local knowledge and experiences, risk models can be built to predict where disease outbreaks are most likely to occur. GPS-enabled smartphones can also track the spread of disease from infected to disease-free areas; these human migration models provide a more accurate picture disease transmission. Digital tools certainly present an added challenge, as they require more maintenance, specialization and expertise, and training. However, they are also beginning to provide key support for more holistic global disease tracking, control, and eradication programs. Efforts to educate epidemiologists, government officials, and field volunteers about these new technologies is a necessary step to ensure their continued use in the fight against communicable diseases.

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In addition to mobile data, volunteered geographic information (VGI) is another growing type of community-based data collection methods. Michael Goodchild first coined this term in 2007 when describing the “widespread engagement of large numbers of private citizens, often with little in the way of formal qualifications, in the creation of geographic information.” This phenomenon, which is manifest in OpenStreetMap and other participatory mapping platforms, has the potential to fundamentally redefine the “traditional role of mapping agencies and companies” (Goodchild 2007). VGI relies on the notion that humans can receive, compile and interpret sensory information and can freely move throughout geographic space. Simply defined, “VGI is crowdsourced geographic information provided by a wide range of participants with varying levels of education, knowledge, and skill” (Fast 2013). This type of geographic information can be gathered from the public using footprint, passive or active VGI mechanisms. Footprint VGI, which constitutes the digital skin of the city, includes credit card transactions and other forms of unknowingly contributed geographic information. Examples of passive mechanisms include tweets and other social media updates in which geographic information is knowingly volunteered but not intended for analysis. Conversely, for active mechanisms, intentional community engagement and involvement are at the center of crowdsourcing geographic information; participatory mapping is an example of active VGI. A VGI system can be implemented in a variety of ways, including base mapping coverage, emergency reporting and citizen science (Fast 2013). OpenStreetMaps is a prime example of base mapping coverage because it relies on volunteers to contribute topographic data through uploading GPS tracks and tracing satellite imagery. In some areas, OSM data is more accurate than maps produced by national mapping agencies because knowledgeable citizens can edit it and the general public can easily access it. VGI can also be used for humanitarian aid, crisis mapping and emergency responding because it effectively harnesses “the power than can emerge from a mass of individuals converging to tackle a set of tasks” (Dodge and Kitchin 2013). For example, Kenyan activists developed the Ushahidi platform after the 2007 presidential election plunged Kenya into political turmoil. This platform, which has since been packaged and made publicly available, allows the public to report relevant incidents to the online map using simple text messages (SMS) or the Internet. It can be employed in a wide variety of circumstances, ranging from national relief efforts to localized community engagement projects. Finally, VGI can also be used in the realm of “geographical citizen science” for community-based monitoring of environmental or social phenomena (Haklay 2013). Evaluating Data Collection Alternatives The emergence of new community-based nonprofits, coupled with a decreasing pool of government funding, has created a competitive culture among community organizations. Increasingly, government and private funding is contingent on quantifiable metrics and technical expertise, which has led to the professionalization of community activism. GIS is one type of tool that can carry out statistical analysis and monitor neighborhood change to satisfy these quantitative metrics. Implementing these spatial analysis technologies has significant ramifications for the ways in which local-level urban political change occurs and how different types of spatial knowledge consequently evolve. Spatial knowledge, or the “characteristics and meanings that individuals, social groups, and institutions ascribe to particular places,” is crucial because it determines the extent to which “the needs, priorities, and goals of residents and

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community organizations are expressed and included” (Elwood 2006). Current participatory GIS literature focuses on technical questions about data accessibility and neglects overarching questions about the sociopolitical implications of GIS. How should community organizations be involved in producing and disseminating spatial knowledge, and what role should GIS play in that? Community organizations use GIS to create flexible, context-dependent spatial narratives and to pursue multiple objectives that transcend cooptation by or resistance to more powerful entities. Community organizations must articulate agendas for social change that effectively advocate for their respective communities. Navigating spatial politics, institutional politics, and knowledge politics is essential for community organizations to maintain their “autonomy and authority” (Elwood 2006). Spatial politics encompasses urban planning, service accessibility, and other fundamentally spatial problems. The spatial scale at which these problems are addressed determines the authority of government, nonprofit organization, and individual actors. By restricting community organizations to explicitly local scales, their authority in urban politics at larger scales is curtailed. From a more practical perspective, the supposed openness of VGI is constrained to those with broadband Internet access. While larger cities in developing countries have growing access to the Internet, much of the world’s population lacks Internet access, and consequently, cannot volunteer geographic information. Additionally, many VGI servers only support the Roman alphabet and English, meaning that alternative scripts cannot be incorporated (Goodchild 2007). However, platforms continue to evolve and although not a specific VGI server, Twitter offers textual tweets embedded with locational information in universally understood geographic coordinates (Elwood et al. 2012). Like most technologies, GIS can marginalize or empower communities, depending on who controls the creation and dissemination of geospatial data and for what purposes these data are used. GIS that actively empowers communities has been labeled “public participatory GIS” or “PPGIS” and can be used to mobilize populations around specific policy issues (Elwood 2006). Because many policy issues are fundamentally geographic or spatial in nature, PPGIS can endow the community decision-making process with greater legitimacy and efficacy. For example, many community-based organizations are using PPGIS to facilitate more effective, justice-oriented neighborhood-level organizing, planning, and problem solving (Williams et al. 2014). Consequently, in contexts where access to standardized, well-collected geospatial data is limited, community organizations cannot effectively advocate for and affect social change. However, without a well-developed GIS system for integrating and managing the collaboratively collected data, PPGIS remains relatively ineffective. Additionally, the data gathered through PPGIS or other crowdsourced data alternatives is not necessarily standardized and of high-quality. While best practice guidelines for “assessing data errors or effective cartographic visualization” have been clearly articulated for the professionalized GIS field, these guidelines do not exist for public participatory GIS (Radil & Jiao 2016). This set of professional practices has been identified as “internalist ethics,” as opposed to “externalist ethics” that deal with broader societal concerns (Crampton 1995). Involving marginalized groups of people in PPGIS is an example of an externalist ethic, and while these concerns are crucial, researchers have only recently made a concerted effort to establish a cohesive mapping and spatial analysis framework for PPGIS. Citizen stakeholders contribute valuable local knowledge to GIS databases and visualizations,

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but their level of technical GIS training and data access limits the effectiveness of these contributions (Radil & Jiao 2016). These technical barriers to participation are contextualized within broader concerns about social dimensions of inclusion based on the participants’ identities. These identity categories include, but are not limited to, class, race, and gender, and often determine whether or not participants interact with the PPGIS project. Parker (2006) described a quintessential PPGIS volunteer group as “middle class, affluent, Caucasian community members who were at least partially invested in environmental causes” (474). The typical demographic composition of PPGIS volunteers may exclude already marginalized demographic groups and further diminish their collective voice in the public sphere. Consequently, “the process of participation with a PPGIS might be seen as itself inherently political, as the various steps of the project … offer potential points of negotiation, contestation, and perhaps even exclusion among various participants” (Radil & Jiao 2016, 204). Because inclusion is central to the aims and benefits of PPGIS, such as increased local knowledge, researchers must focus more intentionally on achieving widespread participant inclusion. Developing a VGI System Until recently, VGI lacked a standardized methodological framework to ensure complete, accurate, and reliable results (Fast 2013). Conceptualizing the systems that produce VGI allow for more knowledge about data acquisition, participant engagement and technical infrastructure. Fundamentally, a VGI system can be conceptualized as “an environment for the production of VGI as an information product” (Fast 2013). The associated components, namely the project manager, the participants volunteering geographic information, and the technical data infrastructure, coalesce to produce VGI, a specific type of crowdsourced information. For the project to function effectively, the technical infrastructure must allow users to input, manage, visualize, and present relevant geographic data. Before initiating a VGI project, the central goals and plan for sustainability must be clearly defined. Without a clearly articulated purpose, the project is not likely to garner strong public participation or produce relevant, applicable results. Next, a geographic study area and participation strategy must be established. Either active or passive participation strategies can be employed, depending on the specific project at hand. For projects involving active contributions, participants are often selected from an engaged group of stakeholders, such as a local community organization or a specific demographic group; these participants are expected to contribute to intentional observation and analysis. Conversely, for projects involving passive contributions, participants act as “observation platform[s]” that gather data without active engagement (Haklay 2013, 112). Depending on the project’s scope, promotional strategies can be employed using social media platforms to increase volunteer contributions. Determining the unique combination of hardware and software components depends on the project’s specific goals and participation strategies. VGI systems hardware can include server and participant computers or location-enabled devices like GPS units and smart phones, and VGI systems software can include “proprietary and open-source platforms,” like ArcGIS Online or Ushahidi (Fast 2013). Interactions between software and hardware occur within the Geoweb, an online environment that pairs geographic systems with location-enabled services. The specific type of technical infrastructure used for a project dictates the functions at hand to create and manage VGI, namely input, management, analysis, and presentation. For example, the Ushahidi

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platform offers unique advantages over other software platforms because it accepts a wide variety of data input methods, including SMS and Twitter, allows the project manager to approve participant inputs and conduct statistical analysis, and produces an interactive web map (Zook et al. 2010). VGI Simulation Methodology Drawing on the methodology outlined in the previous section, I will outline a basic framework for implementing VGI systems in Bahir Dar, Ethiopia, as a case study for VGI feasibility in developing countries as initiated from outside the country. I selected Bahir Dar University in the northwestern Amhara region as the project site because of institutional connections with the geography department at my institution and the newly established Geospatial Technology and Data Center on the campus at Bahir Dar. The participants, namely students and faculty at Bahir Dar University, have access to broadband Internet and location-enabled mobile devices, which will enable participation in the VGI simulation. The central goal of this simulation is to evaluate the feasibility of using VGI as an alternative data collection method for developing countries that lack access to geospatial data. Because the specific topic of this simulation is less important than evaluating whether or not VGI is feasible in Ethiopia and other developing countries, I created an Ushahidi platform based on a simple question: Where is the social center on Bahir Dar University’s campus? While establishing the social center of Bahir Dar University’s campus is not an important or groundbreaking research topic, this simple spatial question can more easily be used to evaluate the effectiveness of VGI than a complex, multi-faceted spatial question. The hardware components of this project are participants’ computers, basic cell phones, and smart phones. Based on the project’s specific parameters, the Ushahidi platform is the project’s software component because it accepts data from a variety of inputs, including simple text messages, location-enabled smart phones, and geolocation-enabled Twitter updates. Additionally, this platform allows for greater control over data management, analysis, and presentation. To create the “Popular Locations on Bahir Dar University’s Campus” Ushahidi platform, which can be accessed at http://socialcenterbahirdar.crowdmap.com, I first configured the types of hardware components participants could use to submit reports. I set up a separate email account, socialcenterbahirdar@gmail.com, for participants to send reports to and also created a Twitter application to collect tweets containing “#socialcenterbahirdar.” Participants can also submit reports from their mobile phones to socialcenterbahirdar@gmail.com if they cannot access a computer. After participants submit reports regarding their favorite places to socialize on Bahir Dar University’s campus, I will approve these reports in order to visualize them on the map. After configuring the reporting settings, I established the geographic area of the map and created the necessary categories for the map. I developed the methodological framework for this simulation in the United States, while my contemporary in Ethiopia, Dr. Daniel Mengistu and other members of the Geospatial Technology and Data Center at Bahir Dar University helped organize participants and implement the simulation. To organize participants, Dr. Mengistu and his colleagues advertised the simulation to undergraduate, Master’s, and PhD students as a way to participate in a geographic research

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project. I sent the following description to Dr. Mengistu and requested him to forward it to relevant students, faculty, and staff:

Texts, emails, and location-enabled tweets are becoming a popular way to collect crowdsourced data from community members and are useful in a variety of environments and contexts. As part of her undergraduate geography honors thesis resarch, senior Annaka Scheeres from Calvin College (Michigan, USA) is using online Ushahidi mapping software to evaluate the feasibility of collecting crowdsourced data at Bahir Dar University (Ethiopia) and Calvin College. You can participate in this research project on Friday, May 6 by submitting your favorite place to hang out on campus through one of the following options: 1) Send a location-enabled tweet including #socialcenterbahirdar from your favorite place to hang out on campus (make sure to enable exact location if sending from a mobile phone or describe the location in body of tweet if sending from a computer), or 2) Send an email to socialcenterbahirdar@gmail.com from your computer or mobile phone that carefully describes your favorite location on campus. Visit http://socialcenterbahirdar.crowdmap.com to view and interact with your results! Thanks for your help!

To run the simulation, students will input geographic data to the Ushahidi platform using their preferred electronic device, and I will remotely approve, manage, and visualize their reports. I also developed a similar methodological framework to run this simulation at Calvin College in Grand Rapids, MI. To promote the simulation on Calvin’s campus, I sent out the description included above and replaced information about Bahir Dar University with information about Calvin: #socialcentercalvin, socialcentercalvincollege@gmail.com, and http://socialcentercalvin.crowdmap.com. By comparing the success of these two different simulations, I hope to evaluate the feasibility of VGI in different contexts and for different purposes. Even though data accessibility is not a central issue in the United States, VGI can still be a useful tool for analyzing the impact of social media campaigns and for gathering data that incorporates the narrative, place-based knowledge of individual participants. VGI Simulation Results The VGI simulation at Bahir Dar University occurred on May 6, 2016, with the help of Dr. Daniel Mengistu and other members of the Geospatial Technology and Data Center. There were low levels of participation, most likely because I did not have face-to-face communication with any of the participants. Only five students and faculty members submitted their favorite place to spend time on Bahir Dar’s campus; all participants submitted reports via email, and several participants included exact geographic coordinates in their reports. Other participants described their favorite place using a phrase or short paragraph. The VGI simulation at Calvin College occurred on May 9, 2016, with the help of Dr. Jason VanHorn and other members of the Geology, Geography, and Environmental Studies department. This simulation generated more responses than the simulation at Bahir Dar

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University, most likely because I was able to more immediately communicate with students and faculty participating in the simulation. Forty students and faculty members submitted their favorite place to spend time on Calvin’s campus; most participants submitted reports via email, but a few participants submitted reports via location-enabled tweets. A few participants included exact geographic coordinates in their reports, but most participants described their favorite place on campus using a phrase or short paragraph. Comparing the VGI Simulations The Calvin College simulation had a higher rate of participation than the Bahir Dar University simulation; 40 students and faculty members reported their favorite place on Calvin’s campus, whereas only 5 students and faculty members reported their favorite place on Bahir Dar University’s campus. First and foremost, this differential success can be attributed to the Bahir Dar University simulation being remotely conducted and the Calvin College simulation being proximately conducted. I was able to effectively advertise the simulation on Calvin’s campus or directly ask certain groups of people to participate. However, after emailing the participation instructions to Dr. Mengistu, I was not able to continue encouraging people to participate in the simulation. This communication barrier ultimately impacted the participation levels of the two simulations. While the simulation at Calvin was more successful than the simulation at Bahir Dar, both VGI campaigns had relatively low response rates compared to social media campaigns that develop organically; this crucial point will be explored in the subsequent section. Another crucial difference between the simulations was my differential knowledge of Calvin College’s geography and Bahir Dar University’s geography. When participants submitted a report specifying “Commons Lawn” or “North Hall 067” as their favorite location on campus, I was easily able to locate this place on the OpenStreetMap basemap provided by Ushahidi because I am familiar with Calvin’s campus. However, when participants specified their favorite place on Bahir Dar University’s campus, I was not easily able to locate this place on the basemap. These key differences between the Calvin College simulation and the Bahir Dar University simulation highlight the importance of familiarity with the local geography and proximity to the research area. The simulation at Bahir Dar University likely would have generated higher participation levels if a Master’s or PhD student in the university’s geography department had organized, advertised, and monitored the process. Critiquing the VGI Simulations Before the VGI simulation occurred, I spent significant time addressing technical problems with the Ushahidi mapping platform. Configuring the settings for Twitter proved problematic because location-enabled tweets did not necessarily translate into exact latitude and longitude coordinates when generating a report. To extract this geographic information from a tweet, the user had to include exact coordinates, instead of only including “Bahir Dar, Ethiopia” or another place name describing a large geographic area. However, the option to include exact coordinates is only available when tweeting from a mobile phone, not from a computer. This limits the overall accessibility and functionality of submitting reports via computer and eliminates the main advantage of submitting reports via Twitter over submitting reports via email, namely the ability to embed exact geographic information in a tweet. While the user can describe a location in an email, she cannot embed exact geographic information in an email. Without the ability to easily

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embed exact geographic information in a tweet, Twitter reports no longer have a strong advantage over email reports. Another noteworthy weakness of the Ethiopia simulation was its geographic location and context. Bahir Dar University most likely has higher rates of mobile phone and computer ownership than more rural areas of Ethiopia, meaning that this type of crowdsourced data collection is not feasible in the majority of Ethiopian communities. Additionally, although Bahir Dar University has limited access to geospatial data, this academic institution has far greater access than nonprofit organizations operating in more rural areas, for example. Food for the Hungry, the food and water security nonprofit that initially inspired this research, has very limited access to stream network shapefiles or other basic geospatial data, which impedes the organization’s overall efficiency and efficacy. Nonprofit organizations like Food for the Hungry can also greatly benefit from community-based data collection methods, but crowdsourced VGI may not be a feasible alternative because of limited access to mobile phones and computers in rural Ethiopian villages. Establishing a collaborative spatial data infrastructure between different nonprofit organizations could greatly enhance their collective ability to affect social change through more effective community water monitoring or food asset mapping. However, a different methodology must be developed that accounts for limited access to technological devices. In the weeks leading up to the VGI simulation at Bahir Dar University, communication was conducted entirely by email because of constraints imposed by time zone difference and geographic distance. Communicating by email proved difficult because responses from Dr. Mengistu were often delayed and relatively brief. It was also difficult to succinctly and accurately communicate the goals and methodology of this simulation via email because of the language barrier. Additionally, because I was remotely running the simulation, ensuring that relevant students, faculty, and staff were aware of this simulation proved difficult. I could not publish flyers or directly encourage people to participate in the simulation, which decreased participation levels and negatively impacted the simulation’s final results. Low participation levels made analysis about the feasibility of VGI limited and inconclusive. While the Bahir Dar University simulation was occurring, I was responsible for remotely approving participants’ reports and locating their favorite place on the OpenStreetMap basemap provided as part of the Ushahidi platform. However, because this basemap includes less detail for Bahir Dar University than for Calvin College, locating the places participants reported proved challenging. Places participants reported, such as Yibab Campus or Peda Campus, were not clearly demarcated on the OpenStreetMap basemap. Because I am not familiar with the local geography of Bahir Dar, locating these places required extensive online research that did not necessarily yield conclusive results. A noteworthy weakness of the VGI simulation at Calvin was the small, biased cohort of participants. Because of my affiliation with Calvin’s Geology, Geography, and Environmental Studies department, students and faculty in this department submitted the majority of responses and skewed the simulation’s results. More than a quarter of participants identified “North Hall 078,” more commonly referred to as the “GEO Common Room,” as their favorite location on Calvin’s campus, making this the most popular location on campus according to the VGI simulation. However, if every student or faculty member at Calvin were required to submit their

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favorite location on campus, the same results would not be generated because Calvin’s Geology, Geography, and Environmental Studies department is relatively small compared to other departments on campus. Another more central, well-known location would most likely be identified as the most popular place to spend time on Calvin’s campus. A final weakness of both simulations was their distinct lack of social or political momentum. People participate in social media campaigns because of an urgent need to voice their opinions about a significant social event or respond to a natural disaster, for example. This sense urgency cannot be simulated through an expert-deployed social media campaign intended to answer a specific research question. Accordingly, participants at Calvin and Bahir Dar were not eager to submit their favorite place to spend time on a college campus because there was no sense of social or political urgency in this simulated campaign. This lack of organic momentum explains the low response rate of both simulations. To effectively conduct a VGI simulation to answer a specific research question, a GIS expert must deploy the campaign and manage the crowdsourced results, and several trained “participant experts” must mobilize average citizens and instruct them how to participate. Without significant encouragement and explicit instructions, the VGI campaign will not generate a high volume of standardized responses to answer the focal research question because social momentum is rarely generated around a highly specific question. Conversely, when social media campaigns arise organically in response to significant social events, such as the Arab Spring in early 2011, average citizens volunteer geographic information via Twitter and other outlets without prompting from a GIS expert. A researcher could create an Ushahidi platform that, for example, collects and visualizes all tweets containing an already existing hashtag. Rather than deploying a social media campaign, this researcher is simply tapping into and collection geographic information from a pre-existing social media campaign. Because this social media campaign arose organically and was not explicitly created for academic research, a large, diverse group of people will most likely volunteer geographic information via Twitter or another social media platform. Both expert-deployed VGI campaigns and passive VGI collection from ongoing social media campaigns are important platforms for gathering VGI and have unique advantages and disadvantages. Expert-deployed VGI campaigns are useful because they can answer specific, focused questions and can generate high-quality, standardized results if facilitated by “participant experts” who have been educated about correct data collection procedures. For example, a water security non-profit in Ethiopia could deploy a VGI campaign for community members to map the locations of wells and report their water quality. However, because a research-oriented VGI campaign is inherently simulated, it does not have inherent social momentum. On the other hand, passive VGI collection from ongoing social media campaigns can tap into pre-existing social movements and, therefore, does not require extensive promotion or explicit instruction. However, data quality and standardization pose significant problems for this type of VGI collection. To visualize tweets using an Ushahidi platform, they must contain exact geographic coordinates, yet few Twitter users embed exact geographic information in their tweets without specific instruction to do so. Depending on the specific type of VGI desired and the purposes for which it will be used, the researcher must decide whether to deploy a unique campaign or simply collect data from a pre-existing campaign.

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Conclusions VGI or other methods of collecting crowdsourced geospatial data can be a valuable alternative when access to geospatial data is scarce or when an organization desires to incorporate local geographic knowledge into a dataset. Although establishing a national geospatial data infrastructure is possible, community-based data collection methods can be a useful alternative for countries lacking or in the process of developing SDIs. The results of this study suggest two main reasonable uses for Ushahidi and other crowdsourced data platforms. The first reasonable use is in response to a massive political upheaval or social injustice, such as Kenya’s disputed election in 2007 or the Haitian earthquake in 2010. After a significant political event or devastating natural disaster, average citizens often desire a platform to voice their opinions or express their current emotional or mental state. However, without a significant event around which to mobilize, people will not gain true ownership of a social media campaign and will not willingly volunteer geographic information without formal prompting. The second reasonable use is an expert-deployed VGI campaign that seeks to answer a focused research question or address a specific problem, such as the water quality of wells in a rural Ethiopian village. In this context, “participant experts” must be trained to help facilitate the VGI collection process and ensure standardized, high-quality data from normal participants. The two VGI simulations conducted as part of this research fall into the second category, since both attempted to gather geographic information about a specific research question. Together, these campaigns suggest that a successful expert-deployed VGI campaign must adhere to several basic parameters. First, the individual or organization who initiates and monitors the data collection must be familiar with the local geography and community. Second, this individual or organization must mobilize the local community using a spatial question that engages a wide variety of community members; mapping food or water accessibility is a potential example of an engaging and impactful spatial question. Third, this individual or organization must work with the local community to establish easily implementable data collection procedures. For large, remotely-run campaigns, the overseeing expert may need to train several participants to become local experts who can facilitate the data collection process in person. If these basic parameters are met, expert-deployed VGI campaigns can potentially generate useful, relevant datasets to facilitate community-based land management, environmental, or health decisions. Over time, grassroots VGI initiatives could ultimately encourage government entities to establish open access, national spatial data infrastructures. Bibliography Bishop, I. F. Escobar, S. Karuppannan, K. Suwararat, I. Williamson, P. Yates, and H. Yaqub.

2000. Spatial data infrastructures for cities in developing countries: Lessons from the Bangkok experience. Cities 17: 85-96.

Chabot-Couture, G., Seaman, V., Wenger, J., Moonen, B., and Magill, A. 2015. Advancing

digital methods in the fight against communicable disease. International Health 7: 79-81. Crampton, J. 1995. The ethics of GIS. Cartography and Geographic Information Systems 22(1):

84-89.

15

Dodge, M. and Kitchin, R. 2013. Crowdsourced cartography: Mapping experience and knowledge. Environmental Planning A 59: 65-70.

Elwood, S. 2006. Beyond cooptation or resistance: Urban spatial politics, community

organizations, and GIS-based spatial narratives. Annals of the Association of American Geographers 96 (2): 323-341.

Elwood, S., M. Goodchild, and D. Sui. 2012. Researching volunteered geographic information:

Spatial data, geographic research, and new social practice. Annals of the Association of American Geographers 102(3): 571-590.

Fast, V. and Rinner, C. 2014. A systems perspective on volunteered geographic information.

ISPRS International Journal of Geo-Information 3: 1278-1292. Goodchild, M. 2007. Citizens as sensors: The world of volunteered geography. GeoJournal 69

(4): 211-221. Haklay, M. 2013. Citizen science and volunteered geographic information: Overview and

typology of participation. In Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice. Sui, D.Z., Elwood, S., Goodchild, M.F., Eds. Springer: Dordrecht, The Netherlands, pp. 105–122.

Nkambwe, M. 2001. Land information systems development and the national spatial data

infrastructure in Botswana. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 34(6): 113-116.

Parker, B. 2006. Constructing community through maps? Power and praxis in community

mapping. The Professional Geographer 58 (4): 470-84. Radil, S.M. and Jiao, J. 2016. Public participatory GIS and the geography of inclusion. The

Professional Geographer 68 (2): 202-2010. Umar, S., H. Uematsu, C. Wieser, N. Yoshida, and A. L. Dabalen. 2015. Data deprivation:

Another deprivation to end. Policy research working paper; no. WPS 7252. Washington, D.C.: World Bank Group.

Williams, S., Marcello, E., and Klopp J. M. 2014. Toward Open Source Kenya: Creating and

Sharing a GIS Database in Nairobi. Annals of the Associate of American Geographers 104(1): 114-130.

Zook, M., Graham, M., Shelton, T., and Gorman, S. 2010. Volunteered geographic information

and crowdsourcing disaster relief: A case study of the Haitian Earthquake. World Med. Heal. Policy 2, 6–32.