By Matt Cassada
Student in Community GIS, Spring 2022
When we initially started the 1958 Athens Census Mapping Project, I had initially a good idea of what I wanted to do since I noticed two distinct features with the Athens Census Mapping Data: population of the entire area along with businesses around the area. Next, I considered the timing of when this mapping took place which was in 1958. During this time, segregation was still a lingering issue across the Deep South, and this was true in both major urban and rural areas. Finally, I noticed that this mapping data also included major businesses around the Athens area and this included pretty much everything that was a business: federal businesses, food, entertainment, religious groups, and many more.
With these central ideas already placed for me, I decided upon the following observations:
With this plan, I decided to then form my central question/argument for the project which is: "Based on the location/concentration of either colored or non-colored owned businesses, does the population distribution of colored and non-colored residents seem different across Athens-Clark county area? Do you also see a population distribution difference based on if the particular resident was either a colored or non-colored resident? Is their a particular business that could be made out where we see the strongest correlation and is their one with the lowest correlation in relation to Athens population data and business location’s?"
With these questions in place, I then progressed into making my own maps. We all started with a excel/point data of the Athens 1958 Census Data, which we got thanks to cleaning out the initial data. I knew that I had to create two initial data sets just for the residents of the area I made two different point data sets, one for colored residents and one for non-colored residents. This proved to be easy since each resident is listed out if they are colored or non-colored residents. I then inputted the point data into ArcGIS and I then signaling out/deleting the data that was needed for each point set. Thus, when I did this, I ended up with two different point data sets for both colored and non-colored residents.
Initially, you do see some distribution differences in the point data. First, we see that the non-colored residents are more spread out across the Athens area and they are not as centralized. This is different for colored residents who were more centralized near downtown Athens and east of Athens. Colored residents were also less dispersed and more organized compared to non-colored residents. They we more organized in that they were more grouped together.
When it came to making the business points, I must first start off and say that given that data wasn't 100% complete. We still had some business points that were not complete and plenty of businesses that were not registered in the excel data. But I still pressed on with my maps since I still had just about over 300-400 businesses I could look at.
To make the point sets for the businesses, I first needed to make some distinctions between businesses. Since I wanted to look at what kind of businesses had the greatest correlation for residents, I needed to focus businesses out of each point data set and give them their own distinction. After looking through each business, I noticed four different business categories:
With these distinctions in place, I implemented the same overall process I did when it came for the residents in Athens. I cross referenced the points that were businesses and centered on those. I then went through each point individually to look at what category they would fall under based on the distinctions I made. I then repeated this process for all four different business categories.
Finally, when it came to patterns that I noticed with the businesses and its correspondence to residents, the one business I noticed that had the most central concentration was near religious businesses, like churches. The one business that had the least appeared to be food businesses, with very little shown correlation between the two for either resident.
In conclusion, this mapping project proved to be insightful to me. As someone who has never lived in Athens, its interesting to see how the demographics and businesses across the city has changed since the late 1950’s. This project showed not only how residential demographics has changed, but also how businesses across downtown Athens have shifted: from more of a rural area to a more college-themed town.
By Elijah Humphries
Student in Community GIS, Spring 2022
When discussing topics of racial justice, generational wealth is a topic of utmost importance, and land ownership specifically is well explored. The “land as liberation” ideal and the historical factors surrounding different movements towards and away from the accumulation of generational wealth in the form of land ownership for the African American population in the South has been tested by various movements. For example, Fannie Lou Hamer’s Freedom Farms sought to return land to Black people in the South and achieve a level of autonomy and independence from a political and economic system that shamelessly segregated and disadvantaged them. But the benefits of land ownership are multiplicative. A landowner gains a larger stake in their community and the ability to slowly build that wealth, then pass it on to their children. By 2021, 27% of American wealth was in the hands of the top 1.0% of the population, this gap has only grown since then, and about 40% of wealth in the US is inherited (Source). So when we consider how, despite movements to fix the divide, generations of African Americans were denied the ability to buy and own land, there’s a clearly forced inequality in that sphere which allows people to build generational wealth.
It was intentional, too. The Housing Act of 1949 cleared the way, quite literally, for a clean sweeping of the urban poor, because it allowed governments to do away with poor or substandard housing in urban areas, called urban renewal, as more affluent citizens moved to the suburbs. With the advent of urban renewal projects, governmental officials had the toolset to clear “blighted” or “dilapidated” areas in their jurisdictions, but these overwhelmingly targeted Black populations and other minority groups. In more recent years, urban renewal practices are viewed as discriminatory across the board. The U.S. Commission on Civil Rights published a briefing report in 2014 titled The Civil Rights Implications of Eminent Domain Abuse, which analyzed two Supreme Court cases, Berman v. Parker and Kelo v. City of New London, looking at the decisions, critiques, and several studies and briefings that identified cases of eminent domain abuse and its effect on poor and minority communities and how that might be interconnected with civil rights. The report noted a 2007 study titled Victimizing the Vulnerable: The Demographics of Eminent Domain Abuse which found a disproportionate impact on communities with the least power: “more residents in areas targeted by eminent domain-as compared to those in surrounding communities-are ethnic or racial minorities, have completed significantly less education, live on significantly less income, and significantly more of them live at or below the federal poverty line.
Here in Athens, one such area was Linnentown, a neighborhood adjacent to the University of Georgia campus. Starting in the 1950s, the University and the City of Athens began making moves to condemn land so that the University could expand. The University of Georgia campus was almost entirely surrounded by residential properties, and had plenty of land to look at for expansion, including through buying it at a fair price, but instead worked to have the Linnentown residents forcefully removed from their homes. First, by making it a completely unsafe place to live, and followed by more aggressive measures, until they were told that they simply could not live in their own homes anymore, that they had been bought by the University at a pittance, and that they would have to find new places to stay. The University refuses to acknowledge its role in these proceedings, or that it participated in what is widely known to be racially discriminatory practices for its own benefit. As students, and people interested in doing what is best for our community, we should keep pushing for recognition and reparation.
Our class is working to create more recognition for the people who seek to right these wrongs. By offering our time and capacity as students, we help put together marketable information that will help more people understand what the University and the City did, and why those actions were detestable and worthy of condemnation. As more and more of the information we consume is transferred over a digital medium, it becomes more important to make history more accessible and clear to others. By contextualizing and narrating the circumstances and proceedings of the urban renewal project that destroyed Linnentown, we bring the issue back to the attention of our peers and contemporaries. I’ve been working on a storymap, a web page that allows anyone to scroll through a narrative experience curated by myself and the rest of the class, that will display much of the information surrounding both the historical events and the contemporary actions we and others have taken.
By Claudia White
Community GIS student and CML research assistant, Spring 2022
This semester, I am taking Community GIS with Dr. Shannon and working with the Community Mapping Lab on various projects, including creating a StoryMap of Linnentown (a Black residential community in Athens displaced by urban removal in the 1960s), digitizing a street network, geocoding addresses, and cleaning data records from a 1958 Athens directory.
One of our assignments was to create a map using the 1958 Athens directory and the street network that we digitized. The city directory included a listing of residents, businesses, and organizations in 1958 and their addresses. Data on residents included several variables, such as occupation, race, the number of dependents, home ownership status. Using the address data and resident data, I decided to create a map about the commute time of maids to look at patterns of travel (time, length, etc.) clustering of living vs working addresses. The current map is hosted on ArcGIS Online, but I first tried to make it in ArcGIS Pro.
Here be the practical things I have learned while making this map.
1) Creating Maps for the Public
There are a million tiny decisions to make when creating maps from scratch. Creating visualizations for yourself is one thing, but creating maps for the general public is another. When creating maps for others, make sure you are creating visuals that are both easy to understand and easy to look at. Keeping design principles in mind is key.
My first map draft had colors that were difficult to distinguish, lines that were hard to separate from the background streets, and symbols that were too small to see (Figure 1).
With Dr. Shannon’s help, I thought about figure-ground, visual contrast, and legibility. To make the map easier to read, I made the background road network more transparent, set route colors to very contrasting colors and put a drop shadow on the routes to make them more discernible from each other and the background. I also made the symbols larger so they were more visible. I then made each maid’s route, living, and working address the same color, so that the viewer could easily see which route and addresses corresponded with each maid (Figure 2).
2) ArcGIS Pro
a) Use snap when digitizing in ArcGIS Pro.
Digitizing images in any GIS software can be tedious, but if you are using ArcGIS to turn an image into features. I highly recommend using the “snap” setting. Turning on snap allows you to minimize topological errors as you digitize (making sure your lines meet up and your polygons are closed. This setting was very helpful when digitizing the 1955 street network for Athens. It made sure that I only created lines that connected to a point on another existing street line. You can turn the snap setting on and off through the dashboard.
b) Depots are origins and orders are destinations.
When creating a driving route in ArcGIS Pro using street networks, you can use the Make Vehicle Routing Problem Analysis Layer. This function will only create a network analysis layer. After creating the layer, you then have to input points that you want to be used to create routes within the network analysis layer. When using sets of origin and destination points (ex: if you want to calculate your commute from your home address to your working address), it is important to note that “depots” are origin points and “orders” are destination points.
c) You can create tiles from raster data using the Create Map Tile Package.
This function uses data from a map pane and compresses it into a tile package that can be used in ArcGIS Online. Make sure the only data on your map pane is the data that you want to tile. Before using this function, you have to add a description, summary, and tags to the data that you are tiling. You must then input the corresponding summary and tags into the geoprocessing pane for the Create Map Tile Package function.
d) You can share map layers as Web Layers for ArcGIS Online.
If you right click on a layer in your map, you can share that layer as an online web layer that can be used in ArcGIS Online. This tool can be used on raster and vector data. You must add a summary and tag(s) of the layer. Once you share the layer as a web layer, the layer is automatically uploaded to your ArcGIS Online content and can be opened, viewed, and stylized in ArcGIS Online. This is particularly useful if you have styled a layer in a particular way because it maintains the layer’s symbology.
3) ArcGIS Online
a) Map Viewer Classic is picky about data users can upload.
When using ArcGIS Online to create a map based on the 1958 Athens directory for this class, I learned that ArcGIS online only accepts certain types of data. In Map Viewer Classic, users can only upload a zipped shapefile, CSV or TXT files, GPX, or GEOJSON files from their files. Of course, users can also use data that is hosted by ArcGIS Online, but the types of data the user can add and manipulate is limited.
b) Map Viewer Classic vs New Map Viewer: Uploading data
In Map Viewer Classic, users are able to add data directly from a file. In the new Map Viewer, users have to jump through a couple more hoops. When using the new viewer, users must first upload their data from file to their content page in ArcGIS Online. When uploading data, you should check the box that adds the desired file AND creates a hosted feature layer. This ensures that you will be able to easily add uploaded data to the map viewer.
c) It does not like raster data.
ArcGIS Online is not a fan of raster data and is not compatible with tiff files. If you are using raster data, first look for any datasets that are already hosted on ArcGIS Online that display the information you are looking for. If you can’t find desired data and must upload an original raster dataset, the best way to do this is by converting your raster data set to tiles and then uploading the tiles to ArcGIS Online.
In creating this map, I learned technical skills related to using different analysis toolboxes in ArcGIS Pro. I also learned how to navigate bounds of ArcGIS online. Most importantly I learned what factors to consider when creating maps for a public audience, including color contrast, symbology, and clear labeling. These skills will help me create more accessible and intuitive visualizations in the future.
By Emilie Castillo
For the past two semesters, I have worked as a CURO Research Assistant in the service of the Community Mapping Lab and BikeAthens towards the goal of creating a comprehensive cycling map of Athens-Clarke County (ACC). While the project will most likely continue into next fall, as of the end of this semester we have succeeded in creating a web-based map of biking routes throughout the county with classifications noting the safety/preference level of each route, bike lanes, multi-use trails, slope, points of interest, and bus stops. This project was undertaken at the request of BikeAthens for the purpose of making cycling in Athens an easier and safer alternate form of transportation. Alongside Olivia Gilliam, a fellow CURO Research Assistant, I worked as an aid to Dr. Jerry Shannon, who headed up this project in the Fall of 2019.
In recent years, Athens-Clarke County Government as well as several community organizations have been interested in building up and refining the cycling infrastructure and resources of ACC. BikeAthens in particular operates with the mission of creating equity in transportation; when they requested the Community Mapping Lab undertake this project, they hoped the map would assist both novice and seasoned cyclists in planning rides throughout the county. That being said, this project was not concerned with classifying the safety level of every existing road or path in ACC, nor was its purpose to showcase recreational rides. The web map, as it is today, displays and classifies those routes necessary for travel throughout the county along with destinations determined as useful or necessary for those using a bike as their main form of transportation.
When Olivia and I began working on this project in Fall 2020, a former student, Regina Nasrallah, had already worked with Dr. Shannon to determine some points of interest and classify some existing road data. Olivia and I began our research by collecting more points of interest (POI). First, we had to determine what were “useful and necessary” destinations for the people in this community and for cyclists in general. Grocery stores, healthcare clinics, bike shops, bike repair stations, bike parking, pharmacies, dollar stores and coffee shops were some of the categories we discussed; we needed to make sure we were being as inclusive as possible when compiling this list because we did not want our map biased towards any particular demographic, rendering it useless to large portions of the Athens community. After determining this list, we needed to collect the location, name, and service type of all the destinations in ACC falling under these categories. I relied heavily on Reference USA, a database of businesses located in the U.S. available through UGA’s Library website. We also extracted several locations from OpenStreetMap. In some cases, a list had already been partially compiled for some destination types, but for the most part we had to search around to make sure we were getting everything. We then organized all these collected data points into a single spreadsheet and mapped it. At this point, we noticed how few points of interest are located to the East of the Loop/ SR10 and we added random location points occurring on the eastside in order to ensure our routes were not missing large chunks of the county.
With the comprehensive POI data mapped, Dr. Shannon used R software in conjunction with Google, HERE, and Mapbox routing APIs to generate approximately 1,500 routes for each routing service between all our identified POI’s (over 600 points). We identified common streets used by those three services and aggregated them to make our first draft routes.
At this point we began to classify the routes, relying heavily on speed limit as an indicator of safety. Later, BikeAthens helped us better classify routes as safe and unsafe from their personal experiences as cyclists in Athens. We were using Strava Heatmap data as a point of reference to determine if the routes we had were ones actually travelled by cyclists and to identify routes we may have missed entirely. I georeferenced screenshots of the Strava Heatmap for the entire county so we could easily compare and edit our data within QGIS.
A large portion of this spring semester has been focused on editing the existing bike routes. Dr. Shannon generated slope data for all the road routes from elevation data provided by Athens-Clarke County. We attempted to gather public feedback through a Survey123 form that allows the user to comment on specific locations of our draft map; however, as the COVID-19 pandemic persisted, public feedback became challenging and often disappointing.
Once we had edited our draft routes to some degree of satisfaction, we began transferring our data from QGIS to ArcGIS Online to begin configuring the web app. We focused on cleaning things up to ensure the map would be easy and intuitive to read. We trimmed down our initial POI data to a smaller pool of points and set the transparency level of that data as smaller than that of the routes so as not to overwhelm the reader upon opening the WebApp. We also began to discuss the best way to color routes, bike lanes, and trails. Further, we determined the best symbols to denote our points of interest and slope. Daniel Sizemore, Bicycle, Pedestrian, and Safety Coordinator from ACC Unified Government, assisted us and is still in the process of organizing data to provide our map with a more comprehensive list of bike lanes. We reviewed several other similar bike route maps of other cities such as Vancouver, Portland, Madison, etc. to get a better idea of how to visualize the data. Dr. Shannon and I worked on creating a print map for BikeAthens staff and patrons to mark up physically, but again, community feedback was difficult and less fruitful than we hoped.
Now, as the semester is ending, we feel that we have a solid draft to start distributing to the public, with the understanding that the routes will continue to be refined and updated throughout the rest of the year. In our final meeting with BikeAthens, we discussed the possibility of one more semester’s worth of work in collecting that much-need feedback from the community. BikeAthens is interested in creating some formats of the map that can be easily printed, possibly even some pocket size maps to be kept at BikeAthens and other biking resource locations.
I really enjoyed working on this project and feel I learned a lot about GIS project management. It was extremely valuable to see first-hand what it is like to work with local organizations and utilize those available resources in showcasing data. Before this experience I would not know how to even start, and now I feel confident in my ability to organize the steps of a project like this and be a part of the construction. The COVID-19 pandemic kept us from meeting in person and facilitating more community events where we could have generated feedback and creative collaboration, and that setback was felt by all of us. Zoom fatigue and generally busy schedules made me feel like I did not engage with this project as whole-heartedly as I could have. A part of me wonders if we had been able to meet in person whether we would have gotten more done in that first semester, giving us more space for creativity in the second semester. All things said, I am really proud of our final product and I hope to stay in the loop about the future of this map and its impact.
Emilie Castillo recently received an undergraduate degree in Geography alongside a certificate in GIS at the University of Georgia.
By Jerry Shannon
Related posts: Aidan's reflection | Katrina's reflection
Many southern universities have faced increased calls to deal with complicated histories of racial exclusion. At the University of Georgia, pressure for the institution to explicitly address the historical legacies of slavery was increased by the 2015 discovery of 105 remains near Baldwin Hall on campus, unearthed during construction of a building expansion. Subsequent testing revealed that many of these individuals had African ancestry, and given the historical period of use for the adjacent city cemetery, this implied that most were likely enslaved. These bodies were reinterred by the university at a nearby active cemetery, but without consultation with leaders from the local African-American community, which caused further tension.
In response, the university has made efforts to acknowledge its historical complicity with enslavement, creating a memorial that honors those buried at the Baldwin Hall site and sponsoring two related research initiatives. The first resulted in the online Athens Layers of Time portal, which provides materials about the Baldwin Hall burials specifically and the historical expansion of campus. The second, currently ongoing, is examining the role of enslaved people in the university’s life from its founding through 1865. UGA also joined the Universities Studying Slavery consortium in December 2019. Both efforts are the result of advocacy by community members and faculty for the university to address historical legacies of enslavement.
Still, the university’s relationship with the local African-American community remains fraught. Only 7.5% of undergraduates and 5% of faculty identify as African-American, while nearly half of service and maintenance staff do. The numbers for student and faculty are far below rates in Clarke County (29%) and Georgia (32%). Specifically, there are rising concerns that new student housing is displacing low-income African-American residents, raising property taxes and putting upward pressure on rents.
The Linnentown Project is one local effort to call attention to these issues. It focuses on the Linnentown neighborhood, a historically black area just west of campus that was demolished in the early 1960s to make space for new student dormitories.Residents of this neighborhood--many of whom were home owners--were most often forced out through the use of condemnation findings and eminent domain. Through public forums and public protests, residents have told the story of their forced removal from the neighborhood, advocating for compensation for financial losses, and further research around the university’s role in slavery and displacement.
Joey Carter, who has helped lead the Linnentown Project, had previously identified multiple records about this redevelopment in special collections at UGA Libraries. These included maps of the neighborhood used to plan property acquisition, correspondence from UGA administrators and legislators about the urban renewal funding used for construction, and detailed records about each property acquired. While he was able to do initial analysis of some of these data, the scanned records had more data than the group could easily enter and analyze.
This spring, students in the Community GIS course offered by Dr. Shannon sought to support this effort through digitizing and analyzing these archival records. More specifically, students in the class focused on the following tasks:
The goal of this project was to crowdsource some of the data entry aspects of this analysis as well as to build a database that could supplement residents’ existing narratives of their displacement. As former resident Hattie Whitehead noted at the beginning of the project, Linnentown was for decades a neighborhood that existed primarily in the memories of those who lived there. By putting it literally “on the map,” our class aimed to provide materials that preserved those memories and provide visual representations that could be used for future education and activism. To adapt Baudrillard’s famous quote, our mapping helped “engender” the territory of Linnentown--giving physical representation to an already existing social reality.
This was, of course, an especially challenging semester for all university classes. In addition to the already existing challenges of coordinating this work among a class of 18 students, we spent a full third of the semester communicating only digitally through Zoom and online discussion boards. Digitizing records for neighborhoods and streets that no longer exist was also a challenging task for the class.
Two graduate students in this course--Aidan and Katrina--have written blog posts about their experiences in the course. In final reflections, other students were clearly impacted by learning about this history and being part of the larger project. Many students have lived in the dorms built on this land, which made it especially personal, and talking with former residents about their experience gave life to the archival data they had been working with. Overall, the goal of our work as a class was to make this chapter of the university’s complicated history more legible and help amplify the experiences and perspectives of Linnentown residents. We hope to further refine these records in the future in coordination with the Linnentown Project.
Jerry Shannon is an Assistant Professor at the University of Georgia in the Departments of Geography and Financial Planning, Housing, & Consumer Economics. He is the director of the Community Mapping Lab.
A few weeks ago, I and roughly 8,000 other geographers attended the annual American Association of Geographers (AAG) meeting in Washington, D.C. While it can be exhausting--AAG has dozens of sessions going on at any given time--these meetings are a great chance to see friends and colleagues and meet new folks whose work I’ve only read or who I’ve only met online. (Despite the platform’s very real problems, I personally have benefited a lot from participating in #academictwitter).
One moment that stuck out: I was talking with Peter Johnson, a faculty member at the University of Waterloo, a morning reception hosted by the Digital Geographies Specialty Group. Peter does great work with open data and governance. For example, here’s one of his recent articles on the costs of open data, including the ways it subsidizes private enterprise and corporate influence on policy. Over the last decade, there’s been a strong push for open data initiatives across multiple levels, from cities up through international bodies such as the UN, which makes this work particularly salient.
Companies such as ESRI and Socrata have created platforms for hosting and sharing these datasets, and the rhetoric around these tools emphasizes transparency and community engagement. Socrata’s page, for example, references a goal of “fully connected communities,” while ESRI touts its “two way engagement platform.” In my classes, I’m particularly fond of letting students analyze NYCOpenData’s records of yellow cab taxi trips, including more than 100 million trips with details down to the tip given for each one.
Peter’s work, along with many others including Renee Sieber, Muki Haklay, Rina Ghose, Taylor Shelton, and Rob Kitchin, has examined how these projects play out on the ground, focusing on whether they live up to claims of citizen engagement and empowerment. As one might expect, results have been mixed. The people most likely to use these data are the ones with the education, training, and expertise to do so--a fairly select group. In my Community GIS class, I use this article on Data Driven Detroit as one example of this dynamic, where open data records on housing only strengthened investors’ ability to buy up vacant property.
The alternative model presented in that article is one I’ve been thinking through as well, community-based projects that facilitate residents’ ability to interact with and make meaning from public data. I asked Peter about this dynamic in our conversation, and he mentioned a project conducted by the Canadian government where trained staff would work with remote rural and indigenous communities, helping them interpret census and other government data and understand their relevance to local concerns. In recent years, the Canadian government has increased the online availability of these data, but it has cut the number of trained staff who can work with local communities. In effect, open data portals replaced these staff, providing more “access” to data but curtailing the work needed to understand and interpret it.
Recent developments in both open source and proprietary software have provided a number of tools for community-based data collection and open data for government records. But, as Alex Orenstein said at our recent community geography workshop, you also need to “check yourself before you tech yourself.” These platforms provide interfaces for accessing and visualizing these data, but they cannot fully replace the important work of helping community members articulate how the data may (or may not) match their own experience.
I’ve been thinking about this in light of my now years-long work with Georgia communities through the Georgia Initiative for Community Housing. Along with my colleague Kim Skobba, I have been helping develop a toolkit for community-based housing assessments, one that uses free and open source software such as OpenDataKit and RStudio’s Shiny platform. These technological tools make it possible for even small rural communities to collect and map out detailed data on individual housing conditions, identifying common issues and facilitating outreach to specific property owners. At the same time, communities struggle with what to do with these data once it’s collected beyond simply noting patterns on the map. Similar to Taylor Shelton’s work in Lexington, I’ve been thinking about ways to work with communities to visualize drivers of problems identified through these data. By talking about landlords, zoning, and other historical factors, we can beging to talk about the problematic history of blight as a metric and its ramifications for community development.
This isn’t work that can be solved by a platform or visualization software. It involves time and “soft skills”--listening, thinking, reading, and many conversations, before the work of data collection even gets started. Community members themselves often want to jump right into the technology, and so it is sometimes difficult to communicate the need to move more deliberately. This is hard labor, but as folks working in public participatory GIS (PPGIS) have long emphasized, it’s crucial to fostering sustainable, just change in communities.
Jerry Shannon is an Assistant Professor at the University of Georgia in the Departments of Geography and Financial Planning, Housing, & Consumer Economics. He is the director of the Community Mapping Lab.
"We do not have weapons, but now we have GIS to protect our territory. We plan to conserve our natural resources for present and future generations through the maps, our maps."
Domingo Ankuash, Shuar Indigenous Leader
During the last five years, campesinos* (peasants) and indigenous people from Southeast Ecuador have started to use maps as a powerful tool for protect their territory against mining companies. Local people believe by mapping their biocultural ecosystem services and territorial boundaries, they are avoiding being cheated by mining companies or by the government. Between Amazon and Andes Ecuadorian region, there is located El Collay Territorial Association, that it is set up by campesino and Shuar indigenous population. 15 years ago, the Shuar communities were the first ecuadorian indigenous population in Ecuador to used participatory GIS. In order to go deeply into campesinos and indigenous Shuar's GIS experience defending their territory, I participated in some community participatory mapping workshops in the El Collay Territorial Association, for the past four years . In this article, I will talk about methodological experiences I have gained over these years and I will show a few maps about biocultural services identified by local communities actors.
El Collay Territorial Association is a political and administrative entity formed by six local municipalities. All six are autonomous, decentralized governments: Paute, Gualaceo, El Pan, Chordeleg, Sevilla de Oro and Santiago de Méndez. This Territorial Association is in the northeastern corner of the Azuay Province in Southern Ecuador. One of the most important inhabitants of this Territorial Association is the Shuar indigenous people. They represent one of the most prominent ethnic group in the Amazonian Region, with around 35,000-40,000 living mainly in the Ecuadorian provinces of Pastaza, Morona Santiago and Zamora Chinchipe, in the southeast of the country. Since 2000, Shuar’s ancestral lands have been assigned for copper mining concessions “for the sake of development”** and, as a result, indigenous communities have suffered persecution and violence. The expropriation of the Shuars’ lands and resources has forced the indigenous community to fight off industrial-scale copper mine and oil extraction and threats to their lands and way of life.
The Shuars struggle to protect their land despite peaceful marches, legal actions, and an international pressure campaign. However, in the last ten years, the Shuars have turned to mapping as a strategy in this effort. Four years ago, as part of my research process to obtain my master’s degree, I started working with campesinos and Shuars indigenous communities. In several conversations, local people pointed out that it is a need to establish community mapping workshops that allow them to delimit their ancestral territories and recognize biocultural services. Later and thanks to local governments and some Shuar leaders support, I managed few meetings and workshops where we gathered an important group of participants that contributed significantly to the project.
My experience in Participatory GIS and Indigenous communities
First, I had some meetings with local actors to explain them the objectives of this project and how Participatory GIS methodology works. There were around 30 participants between indigenous and peasants leaders, that they came from each small villages of El Collay. In a second meeting, I asked the community leaders to make a sketch of their nearest territory, that is, neighborhood or Municipality. Local people had to identify important cultural and natural sites. They used colors, pins and stickers to identify the different types of assets in the community. Once, first workshop finished, I realized that there were major criteria to characterize each small village. Therefore, we used the largest number of responses and create biocultural categories and then units of ecosystem services. As a result, we identified together the categories for important values by local people (i.e. arts, crafts and sacred sites). This outcomes helped me out to make the first map (fig 1). In addition, those decisions over their territory allowed me to integrate each biocultural category as a landscape units in order to give each small village a biocultural unique identity (see fig 2).
The last workshop, I used a local map. In order to avoid bias about indigenous boundaries, I used data from the National Institute of Statistics and Census. I asked them to locate conflict points, that is, environmental, social and cultural problems affecting their territory and changing the landscape. According to local people, the principal territorial problemas are mining companies activities and deficiente local governments administrations. Third, once I finished workshops, I moved to geocoding some biocultural assets exposed by local actors, using ArcGIS 10.5.
Finally, I made two maps about biocultural assets and biocultural ecosystem services. The first map (see fig 1) represents what local actors considered the five essential biocultural elements in their land: archeological sites or sacred sites, traditional skills, food heritage, immovable heritage, and forest (natural elements). Using the biocultural assets map base, I made a map about ecosystem services. So, I grouped characteristics of each small community and made ecosystem services landscape. However, each category can overlap each other, since each village could has all biocultural category in its territory. There are five landscape units: El Collay Forest, Ancestral Knowledge, Sense of Territory, Forbidden Place and the Heritage Food Place (fig 2). The criteria for the cultural ecosystem services classification are the following:
El Collay Forest - This area represents an important element for the El Collay inhabitants, since in addition to be a zone of protection, it is considered a sacred space.
Ancestral Knowledge - It refers to all the knowledge that has been acquired from generation to generation. As for example, the elaboration of crafts, textiles, food preparation, among others.
Sense of Territory - It implies a closeness and an intimacy that is a product of experience, history and time. It demands that people develop an aesthetic sensibility that one gains only when population lives in one place for a long time. This service is mostly located in the Shuar indigenous territory, because for them the sense of territory is linked to the land where their ancestors were born and where future generations will belong.
Forbidden Places - They are sacred places and therefore forbidden to carry out any human activity against natural resources.
Heritage Food - They are characteristic places at local and national level for their gastronomy.
Conclusion and Future projects
There is still much left to do. I plan to continue working over this process in this summer 2019, using counter-mapping approach. The main future objectives are to map sites at risk and conflict, and resilience and resistance areas lead by local communities to mining companies impacts. In conclusion, the maps that have been generated from this participatory GIS process with indigenous and campesinos communities, provide a new way of understanding the world from different worldviews. PGIS maps also demand the integration of these new conceptions of territory, in local territorial planning and national protect biocultural heritage politics of ancestral populations.
*I use campesinos in Spanish because the word peasants has a negative connotation in English language
**This phrase was part of the neo-extractivism speech by ecuadorian government, during 2014
Estefania Palacios-Tamayo is a PhD student in Geography at the University of Georgia. Her research focuses on biocultural landscape dynamics for territorial planning and conservation of local heritage.
As the saying goes: you can’t have your cake and eat it too. In a project for the Community Mapping Lab, I worked with a representative for the United Way of Northeast Georgia’s 2-1-1 program to develop a web application that allows for visual interaction with services offered in Athens, Georgia. Along the way, I had to weigh the pros and cons such as reproducibility and ease of use of different web application development software. I found that it was impossible to have everything I wanted in a single web framework for software development.
The United Way of Northeast Georgia is a non-profit organization that aims to ensure access to quality education, financial stability, and healthy lifestyles for residents of its service region. They work with stakeholders from different sectors such as schools, businesses, financial institutions, and local governments across the state to promote and improve community conditions. Their 2-1-1 program offers residents the opportunity to speak with or text a representative of United Way about services they may be looking for.
In the fall of 2018, I worked with a representative for the United Way’s 2-1-1 program to develop a web application that allows users to locate services offered in Athens, Georgia through a map interface. Although the call line offers callers the ability to speak with someone who is knowledgeable about the services offered, a web application allows users to explore services at their own pace and see details about the services up front as opposed to hearing about them over the phone.
The web application had 5 important requirements:
I looked for ways to meet the requirements listed above, managed data from United Way’s database, and researched the best way I could develop a web application. I did this through an internship with the Community Mapping Lab which actively works to provide students with the opportunity to work with community members as well as apply their knowledge to solve practical problems.
I chose to use ESRI’s Web AppBuilder to develop my web application because it was the best tool for me considering my skill set and complete lack of coding experience. I was able to develop a web application with unique visualization features and useful filtering tools. With this app, the users could explore the agencies in Athens, search for service by language availability, visualize the route(s) agencies are on, access contact information, and determine eligibility.
One of the advantages of my web app is that all agencies which offer services for the general public are listed on its map. Unlike Google or other search engines, the web app shows all the services available from all service categories at once. Google is more likely to display agencies whose names match the keywords input in the search bar. Some of the agency names are not representative of all the services they offer, and because of that, a search engine can be limiting. Additionally, a search engine may not offer as much information as clicking on one of the service icons on my web application may offer. For example, by clicking on a service icon, I can see all the information shown in figure 1, and more.
In a previous blog post, Jiaxin developed her own web application using the Leaflet API to map areas in the state of Georgia that had high percentages of population eligibility for UGA SNAP-Ed programs. Like her, I wanted to develop a web application that allowed users to explore services and visualize eligibility geographically. I know about the importance of using open-source software for reproducibility of a project. I know that ESRI products are expensive to license and thus difficult to access for those who do not have hundreds of dollars to spend on licenses.
So why did I choose the Web AppBuilder? In the end, we have to pick our battles. My app may not be easy to reproduce, but it had the basic features I needed. I wrote instructions on how to download data from United Way’s internal database. I used R to write scripts to prepare the data I had extracted. Even though I was not able to complete this entire project in a way that promotes accessibility to the entirety of my work due to limitations in my knowledge, I gained the skills that could help me produce a Shiny app in the future. It is interesting to consider how “open” open source data or software is if it requires a significant amount of time, knowledge, and experience to be able to use it.
Some features I wish I could have expanded on with ESRI’s Web AppBuilder were fonts, the flexibility in positioning of certain elements and widgets, and further customization of widget features and capabilities (Figure 2). For example, the text below the filters blended in with the background of the filter widget, and I wish I could have chosen a darker color for the text to help it stand out. Additionally, I would have liked to have the legend be on the other side of the web application to allow users to more easily find that button. Finally, I had a difficult time with the filter feature since it only took data in the wide format instead of the long format. These are the sorts of limitations that we can experience when we don’t code our own applications.
In conclusion, in developing a web application for United Way, I made the decision to use ESRI’s Web AppBuilder over R Shiny or Leaflet. There were benefits to ESRI that made developing the web app easy, but I missed out on the flexibility associated with coding my own web application. I struggled with the limits of the ESRI’s Web AppBuilder but learned valuable new skills that I can use to promote more reproducibility of my work in the future.
Aileen Nicolas is a fourth year Geography major at the University of Georgia. She will be pursuing her Master's degree in Geography starting this fall of 2019 also at UGA.
Research has found that retirement is one of three major time points that the elderly (aged 65 and up) tend to move (Litwak and Longino Jr 1987). The reasons for moving may vary: going to a place with better weather, being closer to family members, going to more affordable areas, going to a place with a slower pace, etc. The moving decision is not only related to individual characteristics, such as marital status, presence of children, education level and more, but also associated with the destination community’s characteristics, including the cost of living, climate, amenities, accessibility, and more (Clark, Knapp, and White 1996).
The United States is part of a global trend of counties facing significant aging populations. With the largest elderly population (aged 65 and over) among all developed countries, the U.S. is projected to double its elderly population in 2060, compared to 2014 (Northridge 2012). By 2030, more than 20% of U.S. residents are projected to be elderly, compared with 13% in 2010 (Ortman, Velkoff, and Hogan 2014). The increasing elderly population and proportion of the population generate questions of where and how seniors will spend their last chapter of life. For seniors who choose to move to a new location, what characteristics of the destination are associated with their move? This blog will focus on the southeastern US state, Georgia, to answer the questions about the migration pattern and the migration-related characteristics of the destination.
Using the Census Bureau American Community Survey (ACS) 2013-2017 data, I looked at the elderly migration within the 159 counties in Georgia. The ACS provides data about how many people moved to individual counties from the counties within the same state, from outside of the state, and from foreign countries with age breakdown. Within the five-year period, there were over 47,000 elderly people settled in Georgia, with 24,120 from other states or abroad and the rest moving within Georgia. Figure 1 shows the patterns that the elderly migration in general favor some particular areas, especially the north side of Georgia, including the Atlanta region (29 counties defined by the Atlanta Regional Commission. Other popular destinations are Macon, Augusta, Columbus, Savannah, and other coastal regions. The distribution matches up with the total population distribution.
Next, we then took one step further to look at the proportion of migrant population in the total elderly population for each county. This shows how much of the elderly population recently moved in, and helps to determine what places attract the seniors more after controlling the base population. Counties with high in-migration rates are labeled by name in Figure 2. In general, counties on the south side, especially some edge or neighboring counties of the Atlanta region are with the highest proportion. It may be a result of a balance of affordable living and convenience. The coastal area also attracts seniors. Long County is part of the Hinesville-Fort Stewart Metropolitan Statistical Area, and Mclntosh is included in the Brunswick Metropolitan Statistical Area.
We also apply the multiple linear regression to identify those sociodemographic characteristics and other variables most associated with high migration rates. We included the long-term care facility capacity (number of beds), total population, hospital availability, percent with disabilities, low education (less than college or equivalent) percentage, low racial diversity (using the entropy of race diversity), and more (see the full list of variables in the note). Among all four statistically significant variables (significant level < 0.01), the hospital availability has the biggest positive effect on the migration count, followed by the median house value and total LTC capacity, while the crime rate has a negative influence on the dependent variable. The disability proportion is significant at 0.1 level with a negative impact on migration. Those variables explain about 92.2% of the dependent variable, the raw count of migration population (Adjusted R2 = 0.922). By understanding the associated characteristics of elderly migration, local government and policymakers can better plan the regional development to meet the needs of elderly migrants.
More analysis can be done to separate interstate and intrastate migration since they may be attracted by different regions and different aspects of the destination. Including other variables, such as tax structure of the destination, may add more flavor to this as well. However, it is important to keep in mind that there is information not in the map or available data, thus, there are known unknown parts in this research. Data can only tell the story about numbers, and it will be necessary to have some community engagement to better understand the situation. For example, I will talk with seniors about their needs and concerns in some neighborhoods. As the starting point of my dissertation, these ideas can lead to further dive into the reasons that lying behind these patterns.
The considered independent variables for each county include: total LTC facility count, total LTC capacity (total LTC facility beds), LTC facility beds per 1,000 elderly people, total population (at 1,000), total elderly population (at 10,000), elderly population proportion, hospital availability (hospital count within 10 mile buffer of that county ), male proportion of all age, citizenship proportion of all age, disability proportion, low education (less than college or equivalent) percentage, labor force participation rate, wealthy proportion (ratio of income at or above 400% of the poverty threshold), poverty proportion (ratio of income below 100 percent of the poverty threshold), entropy of diversity, percent rural, crime rate (per 100,000), and the median home value (at $1,000).
Clark, D. E., T. A. Knapp, and N. E. White. 1996. Personal and location-specific characteristics and elderly interstate migration. Growth and Change 27 (3):327–351.
Litwak, E., and C. F. Longino Jr. 1987. Migration Patterns Among the Elderly: A Developmental Perspective. The Gerontologist 27 (3):266–272.
Northridge, M. E. 2012. The strengths of an aging society. American journal of public health 102 (8):1432.
Ortman, J. M., V. A. Velkoff, and H. Hogan. 2014. An Aging Nation: The Older Population in the United States. http://bowchair.com/uploads/9/8/4/9/98495722/agingcensus.pdf.
Author Xuan Zhang is a Ph.D. student at the University of Georgia in the Department of Georgia. Her research uses GIS to investigate the elderly migration and long-term care facility accessibility issues under the umbrella of Health Geography.
Imagine you are a project officer with an international development agency. You are charged with assessing the water resources, quality of access, and management-related challenges of a rural community in Eastern Tibet. You are provided substantial funds by your organization to facilitate a Participatory Rural Appraisal (PRA), an increasingly common method to involve community members in planning, knowledge exchange, and decision-making to address perceived local problems.
You, your development project team, and volunteers from the community work together on a map to document land and water management issues in the region. This map will be a key product for future planning with your agency and will be included in the annual report. One community mapping participant remembers seeing twice as much winter snow accumulating along the mountain ridgeline, just 10 years ago. You put it on the map. Another participant notes the declining abundance of suitable alpine grasslands for their herds of sheep and yak. You put it on the map. Every participant remembers the day they saw a dragon ascending into the sky near a glacier-fed stream. You pause. You don’t put it on the map.
For many Tibetan Drokpa, dragons are real. They’ve seen them. In the positivistic world of western science, a legacy that deeply informs our governmental, non-governmental, and academic institutions, dragons belong to folklore, to myth, and to metaphor.
As makers of participatory maps, I think we need to map the dragon. Beyond metaphor. Beyond folklore. Dragons have a place in this map because they exist in the shared cultural worlds of the map makers. Drokpa knowledge of dragons does not need a western positivist knowledge filter. It does not need to be validated by scientific objectivity, or confirmed under foreign protocols of “data” or “evidence”.
As makers of participatory maps, I think we need to challenge the space of assumptions associated with other cultural realities. Beyond fiction. Beyond myth. I think we need to interrogate the epistemological foundations of our institutions, and recognize that the edge of our maps of knowing may be the beginning (or center) of somebody else’s. After all, there are no neutral ways to represent “reality” on a map; any “reality” depicted is largely informed by ones’ intellectual and cultural predecessors.
In “Dragons, Drokpa, and a Drukpa Kargyu Master”, Diane Barker, recounts testimonies of those who have seen dragons in Tibet, positioning them alongside stunning depictions by Choegyal Rinpoche. Her article makes me pause. It forces me to re-consider the perspectives and worlds deemed legible in academia, and the constraints of the technologies we employ to help compartmentalize and categorize our complex world. Maps and map making can help us to visualize spatially complex interrelationships between social and natural forces. Relationships between water scarcity and elevation, for example, or grassland abundance and shifts in human land-use over time. Maps produced with Geographical Information Software (GIS) can take us even further and help us to measure these complex interactions by experimenting with scale-dependent variables and spatial layers. GIS, as such, is a powerfully important spatial toolset for map making. It is, however, worth recognizing both its technical and epistemological constraints.
Rundstrom (1995) suggests that “GIS technology, when applied cross-culturally, is essentially a tool for epistemological assimilation, and as such, is the newest link in a long chain of attempts by Western societies to subsume or destroy indigenous cultures”. Perhaps it is, in certain contexts. This point is considered in depth by Dr. Kenneth Bauer (2009) who notes that embracing GIS, and the worlds we create through mapping, means embracing a “mode of thinking”.
Bauer argues that “one’s knowledge of the environment lies not in the ideas in our heads but in the world that our predecessors reveal to us”. If our intellectual predecessors are international development officers, who focus on the material and societal needs of the “developing” world, not only will our maps reflect these priorities, but the edge of our maps will hold epistemologically particular metaphorical dragons. If our predecessors are geospatial scientists, many of whom focus on the scalar dynamics between social and natural systems, the edge of our maps will hold equally specific metaphorical dragons. And if our intellectual predecessors are nomadic Drokpa herders, the center of our maps might include real, non-metaphorical dragons. Then, the edge of our map, the boundaries of our known world, may hold something entirely different. Something as foreign as Participatory Rural Appraisal (PRA). Something as foreign as “development”, “geospatial science” or “conservation”.
“Dru gu Choegyal Rinpoche's painting of a dragon sucking up water from a stream in Tibet, 2012” Dragons, Drokpa, and a Drukpa Kargyu Master
In the end, local Drokpa knowledge of dragons may not be commensurate with western knowledge mapping traditions; spatial frameworks that we, as academically-inclined map makers, can know and interpret: 2D, cardinal direction, cartographic maps. Unless we expand our definition of “map”, perhaps Choegyal Rinpoche’s paintings can simply remind us that the edge of our mappable world does not mean the world’s end. Certain cultural realities and worlds of knowing may simply be invisible to us, unless we choose to radically challenge our own preconceptions, trusting and supporting the deeply held realities of our community mapping partners.
Indeed, there are different worlds in each of us. There are also shared cultural worlds that invisibly govern our institutions, design our technologies of visualization (i.e. GIS), and condition what we deem “mappable”. What if, when reaching the boundaries of our own mappable knowledge, we consider how to support other worlds of knowing in our work. We must ask ourselves how we diminish other worlds of knowing by assimilation into our own. Perhaps we can recognize our privileged positionality as map-makers and practice radical epistemological reflexivity, challenging our categories of “data” and “evidence” to produce new maps. Maybe we map the dragon. As mappable as increasing annual glacial snow melt. As mappable as declining range and extent of alpine grasslands.
But can we truly re-consider and re-evaluate our core perspectives, biases, and beliefs during this process? The worlds we know and occupy? Perhaps not completely. What’s more, would such radical reflexivity necessarily dis-empower our scientific perspective in a post-truth world? I don’t think so. I think it broadens our capacity as social scientists to engage in and practice epistemological humility rather than epistemological assimilation.
In my research in Bhutan, known as the Land of the Thunder Dragon, we use participatory mapping as a medium to talk about spatially-explicit, place-based deities, spirits, and divinities that reside and preside over forests, lakes, trees, rivers, and mountains. These more-than-human beings have significant bearing on the ways people make land-use decisions, and conceptualize foreign concepts of development, conservation, and natural resource management. By including dragon sightings in the Drokpa community map, without pause, without filter, our Participatory Rural Appraisal (PRA) will not simply pay lip service to aspirations of “participation”. Instead, the map will be a better reflection of the different worlds that reside in each participant, and more representative of the worlds inherited by our intellectual predecessors.
When the map is complete, it will inevitably be incomplete. Maps will always hold unknowns & uncertainties, assumptions and biases, at their edges. If our aim is to challenge these assumptions, we must put the dragon on the map. Beyond myth. Beyond metaphor. We must challenge who has the power to define the “we”: the voices and viewpoints at the table. A map of this type, however partial, may be a stepping stone to increasingly egalitarian representations of our respective cultural worlds: as academics, international development officers, geospatial scientists, and Drokpa herders.
David Hecht is a PhD candidate in the Integrative Conservation & Anthropology program at the University of Georgia. His research explores the intricacies of sacred landscapes and lived religion in relation to community-based conservation programs for priority bird species in Bhutan. Follow him on Twitter at @davidmhecht.