By Jenny Gallucci
[Note: This was Jenny's second time working as a CURO student in the lab. In her first fellowship, she assisted in collecting data on gentrification and housing displacement in Athens' Hancock neighborhood.]
Fall semester 2019 concluded my second and final experience with CURO, a program providing undergraduate students with funded independent research opportunities, at UGA. This time around with CURO, my work focused on creating a large print map of agencies offering services for those experiencing homelessness that would hang on the wall at the Sparrow’s Nest, an agency providing help to those experiencing housing transition. An online map of these services was created in Dr. Shannon’s Community GIS course in the spring and is currently accessible via the Sparrow’s Nest website . Jamie Scott, the director of Sparrow’s Nest, expressed there was a need for a print map for those who are not computer literate.
I re-created a map of services using data collected by Dr. Shannon’s students, complete with bus routes, parks, rivers, and roads. I then used PowerPoint to arrange the map and other elements such as agency information and contextual information into a printable 36 x 24 in. poster. I largely based the format of my poster off a downloadable map of services on United Way’s website, created by another CML student in years past, although my final product ended up deviating from that template a bit.
One thing that CURO has repeatedly illustrated to me is that it takes a village; reflecting on this semester, I reached out to and collaborated with five people from completely different offices/walks of life to get this map done. I contacted Daniel Sizemore at the ACC Park Planning office to get the parks data set, visited Meagan Duever in the UGA map library several times to solve some GIS issues that came up, and asked Adam Salway, who now works for Wesley Church, for some details about how he made his map, which I followed as a template. I of course also collaborated with Jamie Scott of Sparrow’s Nest and Dr. Shannon to create a cohesive final product. I’ve really come to understand the importance of knowing who to reach out to and not being afraid to ask for help when you need it. The connections I’ve made through internships and classes during my time at UGA really benefited me as I was conducting independent research. CURO has really taught me how to effectively troubleshoot my problems and tackle them in the most effective way, which is without a doubt going to be a skill that I’m going to take with me into any career I chose to enter.
This semester’s CURO project proved to be different than my first project technically; the Sparrow’s Nest project was largely a cartography project, unlike my project last year, which was less primarily focused on aesthetics and more a mix of transcription and qualitative data collection in addition to map-making. Through my project, I started to learn some more cartography basics and programs best suited for cartographical work. I have found that CURO opens doors for me—I wasn’t really aware of the ways geographers use Adobe Illustrator to make high quality maps and the extent of the world of cartography. Both years, CURO has allowed me to explore the intersection of GIS and community, showed me the potential of what’s possible, and reminded me of just how much I have left to learn. I’m so appreciative of everyone that has helped me on the journey through these projects and the unique experience CURO has been.
Download a PDF of the map Jenny made
By Jerry Shannon
This spring marks the fifth anniversary of the Community Mapping Lab. This lab has changed a lot during this time. In the first years, we held regular meetings--primarily with graduate students--to discuss readings and share thoughts about community engaged geographic research. These meetings have become less frequent over time, but undergraduate involvement in the lab has increased considerably, primarily through support from UGA's Center for Undergraduate Research Opportunities (CURO), the Georgia Initiative for Community Housing (GICH), and the Community GIS course. CML has partnered with twenty-five organizations, including local community groups, non-profits, and local government. We've dipped our toes in the water of a few training sessions, including an upcoming training at the Integrative Convervation Conference--watch this page for more details. Because we're a diverse group with a wide range of interests and backgrounds, we started this blog just over a year ago as a forum for a collective conversation about doing community engaged work. In short, we've moved from early exploratory conversations to more structured opportunities for work with a wide range of partners.
More specifically, last fall three CURO students partnered with community organizations around a range of issues:
This spring, Community GIS students will be partnering with the Linnentown Project, which is telling the story of an African-American community near the UGA campus that was demolished in the early 1960s to make space for student housing. Working with archival documents and oral histories, we're hoping to build an archive of materials that tell the story of this neighborhood and speak to current issues around displacement and the legacy of racial discrimination here in Athens.
As the lab continues to grow, I'm hoping we will be able to create a local advisory group of community members that can provide input on our work, and I'd also like to diversify the voices on this blog to include partners outside UGA. Our work here also informs work I'm doing nationally to develop community geography as a subdiscipline, including the formation of a national group, future meetings like #commgeog19, and a special issue of the academic journal GeoJournal. Five years is a great achievement, but in many ways, it feels like we're just now getting started.
I’ve spent the past year working for a unit on campus that connects university resources to rural communities throughout the state. Overall, the work is enjoyable, refreshing, and informative. I get to apply GIS skills to a range of projects for organizations in small towns on issues they’ve identified as important but lack the resources to complete. One of my latest projects was analyzing crime data in Pierpoint, Georgia (editors note: Pierpoint, GA is a pseudonym). The project began as a local economic development tool to show that in fact, this community was not a place with significant numbers of violent crime, but in the end it altered the policing routes. This unanticipated uptake of my analysis has left me wrestling with what success might look like using GIS to map crimes? And what is failure? In this blog post, I share some of my reflections from the experience and outline some of the challenges unique to doing this work in rural communities.
Given the well-documented connections among race, crime, policing, and GIS, this project made me a little nervous from the start, however, policing was not the impetus for the project. Pierpoint’s motivation for the project was that a business informed city leaders the town’s ‘high crime rate’ was among the reasons they didn’t locate to their community. In Pierpoint, negative web-based crime ratings (from places like Trulia or Area Vibes) were leading businesses to seek other areas entirely and having real economic implications. Debunking one of these online ‘sources’ for crime analysis was an exciting proposition to me.
Trulia's crime 'hot spots' in Athens, GA, home of the Community Mapping Lab
According to one website, Pierpoint was a place of violent crime, with murder rates more than double the state of Georgia and the national average. Surprising to me, the rate was accurate, but there was a small numbers problem: the town had two murders in the past three years. This points to a broader issue, namely that data analysis in rural communities has multiple challenges not present in large metros. One, census data is ill suited for small areas. The county has less than 20 census tracts. The municipality, my focus, consists of only five census tracts--three of which extend beyond the jurisdictional boundary. And, most of the ACS demographic data at the neighborhood level has 10-30 percent margins of error, meaning calculating rates or doing any sort of areal interpolation is fraught with accuracy issues.
Second, small rural communities are short staffed and under-resourced following decades of state funding roll backs and market-based municipal governance. When a project like this comes from local leaders, a new task is heaped on an already overworked employee who must pull together three years of data for a project they know little about. This means that data quality, communication, and feedback are often inconsistent.
Lastly, these projects are full of competing power relations to work through. As the ‘technical expert’ from the land grant university, I noticed employees often deferred to me even when I tried to glean insight from their local expertise and sought their feedback on drafts of the report. At the same time, I am not an autonomous actor. My grad student assistantship hinges on completing projects identified by the communities I work with.
In the end, I provided a report to the police department showing the spatial trends of crime types over the past three years. I found out a few months after I submitted the report that the department made changes to their officers’ routes as a result of my analysis. This was not a suggestion I made or the initial motivation for the project. Yet, my work informed this decision. In this way, I failed to one, consider the power of maps and visualizations once they are floating around in public; and two, account for the existing biases and assumptions members of the community might bring to the data I shared.
The project began as a local economic development tool to show that in fact, Pierpoint was not a place with significant numbers of violent crime, but in the end it was simply used as a way to better target law enforcement efforts, perhaps putting already vulnerable communities at further risk. In other words, the impetus to demonstrate that Pierpoint was not a place of violent crime potentially added to the structural violence marginalized groups face. In this way, counter mapping efforts such as Million Dollar Blocks offer an important dimension to crime analysis.
My experience in Pierpoint has challenged me to find ways to build stronger relationships with the communities before I hand over data and consider a project complete. I will also use more caution when I present to community groups in future projects. In a more general sense, I’m curious about how I can meet community-driven project goals but also enfuse critical perspectives, and in particular, how I can better teach critical cartography ‘on the fly’. I welcome feedback from others engaged in similar work and wrestling with similar questions.
Taylor Hafley is a PhD Student in the Department of Geography at the University of Georgia.
My 15 minutes of fame happened a couple of years ago, courtesy of a tweeted map of all the Waffle House locations in the path of the 2017 total eclipse. It took maybe 45 minutes to put together, but has since gotten over 150,000 views. I was interviewed about it by CNN and the Chronicle of Higher Education, and CBS almost sent a film crew down to Athens. One local radio station played the University of Georgia fight song at the end of our interview. It was surreal.
It’s a silly map. But in a just-published article with Dr. Kyle Walker at Texas Christian University, the two of us dug into what makes maps like this one go viral. In addition to developing the extremely useful tidycensus package for R, Kyle used Mapbox to create this interactive dot density map of educational attainment. In this article, published in the Professional Geographer (full text here), we analyzed reactions to the Waffle House and educational attainment maps to better understand the factors that make a viral map. Here’s our argument in brief.
First, drawing from work in communication studies, we write about maps as a form of phatic communication, a form of small talk that may seem inconsequential but that builds connection and affirms certain social identities. Watching particular memes circulate through social media is another example of phatic communication, as each share or retweet affirms a shared experience or identification with particular ideological views (see Anthony Robinson’s excellent recent article for more on the latter point). In this sense, maps can be another “Which Harry Potter Character Are You?” quiz, but about space.
Second, we noted the disjunction between the ways academics and the broader public use maps. While the former often focus on the big picture and general patterns, reactions to our maps showed how non-academic readers used maps to place themselves--to find the closest Waffle House for the eclipse, for example, or to understand the educational profile of their specific neighborhoods. These readers used maps as technologies that named the landscape and told them where they were and where they wanted to be.
This is true not just in a utilitarian sense--maps are more than a way to get from point A to point B. Rather, the emotional reactions to our maps demonstrated the personal connection readers made to the stories each map told. The Waffle House map, for example, converted a cosmic event into a celebration and reaffirmation of southern identity by connecting it to this distinctively Southern chain. In this sense, it’s similar to the “Boo y’all” signs that show up around my Southern college town each October, ones that similarly “localize” Halloween. In contrast, Kyle’s educational attainment map received critical responses focused either on what readers saw as an overvaluing of higher education or on the literal accuracy of the map itself. In both cases, readers reacted to the ways these maps placed them and their identified community.
To slightly adapt Haraway’s famous phrase, these maps were read from somewhere. Maps go viral when they provide readers a way to name the world and themselves in emotionally resonant ways. Depending on context, reactions to viral maps can vary from celebration to anger, from a celebration of shared experience and identity to a rejection of implied judgment and social differentiation.
Decades of research in critical cartography provide examples of the power of maps to name the world. But, I would argue, the rapid rise of social media and online mapping alters the dynamics of this process. Maps can circulate like memes, and online mapping tools like the educational attainment map provide interactive and easily shared ways to craft spatial narratives. For aspiring viral cartographers (especially those of the critical bent), the trick is creating visualizations that facilitate the process of identification but also reframe pressing social problems--for example, improving access to housing and health care or mitigating the drivers of climate change--in unexpected ways. Maps like these don’t focus on the 30,000 foot view. Rather they provide ways for readers to see their ground level reality in a new context, creating moments of recognition and also reorientation.
I’ve thought about viral cartography primarily in work with the Georgia Initiative for Community Housing, where I’ve had the opportunity to work with groups throughout the state on community housing assessment. In creating tools to visualize the data collected by housing teams in those communities, I and my collaborators have been thinking about the ways we can contextualize and analyze those data in constructive and equitable ways. Taylor Shelton’s work in this vein with the city of Lexington has gotten some well-deserved attention, and this semester I’m using a similar approach working with an undergraduate to identify the role of rural landlords in shaping local housing conditions. The goal is to move beyond a broken windows framing, which tends to stigmatize marginalized communities, to something more structurally focused: policies and programs that are supportive and inclusive rather than punitive. This isn’t viral cartography, but there’s a similar need for mapping methods that support both recognition and reorientation. For local housing teams, this is their community and their data, but we’re working to build a process that helps them see both in new ways.
My waffle house map may be history, but research into viral cartography is still just getting started. As Kyle and I wrote at the end of our article, “As geographers develop ways to connect with broader publics over pressing issues such as racial segregation and climate change, developing tools and approaches for viral cartography could provide an effective (and affective) pathway to build trust, forge new partnerships, and foster productive conversations.”
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.
Taylor Hafley, Jerry Shannon, Aileen Nicolas, and Jon Hallemeier
In a controversially entitled NYT op-ed, Maps Don’t Lie, Charles Blow argues President Donald Trump has no regard for the truth. Blow frames his argument around ‘sharpie-gate’--a reference to the President’s marker-modified map of Hurricane Dorian’s path--and places his trust in maps, writing “because of [the] unyielding commitment to accuracy, I believe cartography enjoys an enviable position of credibility and confidence among the people who see it. If you see it mapped, you believe.”
We certainly appreciate NYT’s op-ed page highlighting the value of geography and mapmaking, but as a group committed to investigating the hidden assumptions behind maps, Blow’s argument gives us pause. By anchoring his sharpie-gate critique on cartographers’ commitment to ‘accuracy’ and ‘precision,’ Blow situates his argument on shaky ground. Maps may be factual, but they can also certainly deceive us--no sharpie required.
We commend Blow for defending cartographers so strongly, but as Mark Monmonier recently restated, maps lie, and they do so regularly. Below, we identify three reasons to hold back from the “maps don’t lie” defense.
Mutilated maps can be good
Blow says he found the “mutilated map...just too much’, arguing that mapmaking has a long commitment to factual accuracy and that the work of cartographers should not be defiled. This puts professionally-created maps on too high a pedestal - maps can and sometimes should be "mutilated." For example, the same sharpie pen might be wielded by a meteorologist from the National Weather Service to communicate real-time changes to weather models. Or, in another context, a community member might "mutilate" a map created by cartographers because it is missing critical pieces known only by locals. Indeed, maps are not merely visualized facts. Rather, they present robust narratives about the world and the processes that shape it. These stories work in part due to mutual trust in the maps and mapmakers. As the Floating Sheep collective argued, sometimes “Pretty maps are better than ugly maps, but ugly maps do in a pinch.”
Beautiful maps can mislead
Second, maps can be accurate, marker-free, and enable a person to tell the story of their choosing. For example, Trump’s fascination with electoral college maps has been well documented. One hangs in the West Wing and according to the Washington Post, Trump was handing out copies of the 2016 electoral college map several months into his presidency, saying his election was ‘redder than an electoral victory had ever been.’ Yet the same data can look quite different when controlling for population, as Kenneth Field’s gallery of election maps demonstrates. The president’s favorite map doesn’t lie outrightly, but it certainly provides a misleading view of the political landscape.
Maps tell stories that are selective
Third, maps provide partial facts and shape public understanding through a cartography of omission. Maps contribute to larger, simplified narratives which have real-world consequences. For example, maps of crime or school rankings on real estate website may shape homebuyers’ decisions. Yet these maps leave out a long history of policies from redlining to zoning that have created and maintained a deeply segregated landscape, and in doing so contribute to the reproduction of those same socioeconomic divisions.
Conversely, maps are at the same time instrumental in challenging these histories and omissions. For example, San Francisco’s Anti-Eviction Mapping Project or this map of white collar crime zones show how maps can provide counternarratives that shape public conversations about neighborhood identity. The point isn’t to make a map that tells the whole story, but a map that tells the right one. What is the right story, we might ask? Therein lies the issue, as this is a political and ethical question, not an empirical one.
We applaud the painstaking work of meteorologists who create maps that save lives, and we agree that maps should strive for factual accuracy. Even so, every map’s story is always partial, involving questions such as how to demarcate uncertainty and risk. A sharpie may even sometimes be needed. In this light, the president’s Alabama hurricane may provide a lesson on how to read maps. Rather than decrying the mutilation of ‘truthful’ maps, we should look at all maps with a critical eye, seeking to understand the stories they tell and obscure.
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.
Over the decades, researchers have increasingly looked into the effect of neighborhood stores and other food options on residents' health (Caspi, Sorensen, Subramanian, & Kawachi, 2012). Many researchers have found that food environments play an important role in individuals’ health outcomes (Bleich, Jones-smith, Wolfson, & Zhu, 2015; Cummins, Macintyre, & Glasgow, 2002; Willett, 1994). The Supplemental Nutrition Assistance Program Education (SNAP-Ed), funded by the USDA’s Food and Nutrition Service, also aims to improve the nutrition and healthy lifestyle knowledge of individuals who are SNAP participants and low-income individuals eligible to receive SNAP benefits or other federal assistance. Agencies in each state contract with the USDA to provide classes in nutritional education and sponsor initiatives to encourage healthy food choices. The University of Georgia (UGA) SNAP-Ed program is a collaboration between the Department of Foods and Nutrition and Cooperative Extension that aims to help low-income populations in Georgia establish healthy eating habits and a physically active lifestyle through evidence-based nutrition education and local campaigns to promote consumption of healthy foods. This includes online content showing cooking and shopping tips, advertising campaigns, and outreach to K-12 schools.
During my Master’s degree work, I was a research assistant for the UGA SNAP-Ed program. I worked with Dr. Jerry Shannon to provide spatial analysis and mapping that supported UGA SNAP-Ed program. Using GIS technology, we mapped out areas with high percentages of eligible populations where more than half the population’s income was below 185% of the poverty rate, areas within one mile from Free and Reduced Meal School, Georgia Promise Zones and Georgia Strike Force zones. Sites intersect with any of these areas above are considered as qualified nutrition education sites and are eligible for SNAP-Ed programming.
This tool provides basic map visualization functions for different SNAP-Ed qualification layers, functions for determining nutrition education site eligibility, and an info box updating site qualification information.
Details on the application
Our web tool has five fundamental features:
Basic functions such as adding basemaps, layer controls, and adding info window are well-explained in the tutorials and documentations. For me, the major challenge of this map was determining site eligibility and creating the Geo-locator. While Leaflet is a powerful map visualization API, it has limited features on spatial analysis functions. Luckily, we have plugins contributed by other people to realize those functions. This is one of the many advantages of open source software. To determine whether a point is intersected with a layer, we used the “Leaflet-pip” plugin which provides us point-in-polygon calculation support. For the Geo-locator, we used “Leaflet GeoSearch” plugin which is an easily extensible plugin supports address searching and real-time geocoding in Leaflet.
This web tool is hosted on GitHub’s online repositories using GitHub Pages. GitHub is an open source web-based version control hosting service, it is free and very convenient for project collaboration and distribution. Click on this link to see it live. For more detailed information and actual code, please visit the GitHub page on this project.
Overall, this web tool using geospatial data can help UGA SNAP-Ed determine nutrition site eligibility more efficiently without communicating with GIS experts. The GitHub repositories can also help UGA SNAP-Ed manage the tool in the long run. Also, the open source code and public accessible data can be easily extended and replicated for SNAP-Ed in other states.
One thing worth mentioning here is that even though we provided this tool for UGA SNAP-Ed program, they seldomly used it due to various reasons: First, they still feel better having GIS experts directly confirm eligibility manually. Also, the map is not an adequate stand for specific circumstances. It raised my concern that while our lab has talked a lot about Public Participatory GIS and Community GIS, how to gain public acceptance and further public engagement to it still remains a question.
Yangjiaxin Wei is a first year Ph.D. student at the University of Georgia in the Department of Geography.
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.