In 2021, the City of Atlanta and Atlanta Police Foundation launched joint plans to build a large police training facility in the South River Forest in unincorporated DeKalb County, GA. At this time, residents of Atlanta and DeKalb County, environmental activists, police and prison abolitionists, and other activists and concerned individuals formed the movement in opposition to the facility, known as the Stop Cop City / Defend the Atlanta Forest movement. Social media and digital maps became common tools for communicating information about the facility and the movement. In this work, we examine online maps about the facility and the opposition movement, originating from grassroots organizations, the City of Atlanta, news media outlets, the Atlanta Police Foundation, and individuals. We gather and examine 32 publicly available maps collected through the Google Search API, Twitter (now X), Instagram and reddit. Then, using a framework of critical cartography, we conduct a content analysis of these maps to identify the mapping technologies and techniques (data, cartographic elements, styles) used by different stakeholders in the construction of the facility and roles that maps and mapping technologies can play in social movements. Finally, we examine the extent to which these maps provide data to confirm or dispute concerns raised by grassroots organizations and local residents about the facility. We argue that documenting the use of maps to communicate information about a contentious project can help enumerate positions and perspectives about community issues. We find that the different uses of (and varied access to) geo-spatial technologies is uneven across stakeholders and mapmakers and advocate for accessible mapmaking tools. We conclude by discussing the implications of accessibility of mapping technology and posting maps to social media, and share example map images that extend the geographic information systems (GIS) techniques seen in the retrieved maps.

Choropleth maps are a common and effective way to visualize geographic thematic data. Although cartographers have established many principles about map design, data binning and color usage, less is known about how mapmakers make individual decisions in practice. We interview 16 cartographers and geographic information systems (GIS) experts from 13 government organizations, NGOs, and federal agencies about their choropleth mapmaking decisions and workflows. We categorize our findings and report on how mapmakers follow cartographic guidelines and personal rules of thumb, collaborate with other stakeholders within and outside their organization, and how organizational structures and norms are tied to decision-making during data preparation, data analysis, data binning, map styling, and map post-processing. We find several points of variation as well as regularity across mapmakers and organizations and present takeaways to inform cartographic education and practice, including broader implications and opportunities for CSCW, HCI, and information visualization researchers and practitioners.

During the COVID-19 pandemic, a number of data visualizations were created to inform the public about the rapidly evolving crisis. Data dashboards, a form of information dissemination used during the pandemic, have facilitated this process by visualizing statistics regarding the number of COVID-19 cases over time. Prior work on COVID-19 visualizations has primarily focused on the design and evaluation of specific visualization systems from technology-centered perspectives. However, little is known about what occurs behind the scenes during the visualization creation processes, given the complex sociotechnical contexts in which they are embedded. Yet, such ecological knowledge is necessary to help characterize the nuances and trajectories of visualization design practices in the wild, as well as generate insights into how creators come to understand and approach visualization design on their own terms and for their own situated purposes. In this research, we conducted a qualitative interview study among dashboard creators from federal agencies, state health departments, mainstream news media outlets, and other organizations that created (often widely-used) COVID-19 dashboards to answer the following questions: how did visualization creators engage in COVID-19 dashboard design, and what tensions, conflicts, and challenges arose during this process? Our findings detail the trajectory of design practices-from creation to expansion, maintenance, and termination-that are shaped by the complex interplay between design goals, tools and technologies, labor, emerging crisis contexts, and public engagement. We particularly examined the tensions between designers and the general public involved in these processes. These conflicts, which often materialized due to a divergence between public demands and standing policies, centered around the type and amount of information to be visualized, how public perceptions shape and are shaped by visualization design, and the strategies utilized to deal with (potential) misinterpretations and misuse of visualizations. Our findings and lessons learned shed light on new ways of thinking in visualization design, focusing on the bundled activities that are invariably involved in human and nonhuman participation throughout the entire trajectory of design practice.

The American Mafia is a network of criminals engaged in drug trafficking, violence and other illegal activities. Here, we analyze a historical spatial social network (SSN) of 680 Mafia members found in a 1960 investigatory dossier compiled by the U.S. Federal Bureau of Narcotics. The dossier includes connections between members who were ‘known criminal associates’ and members are geolocated to a known home address across 15 major U.S. cities.Under an overarching narrative of identifying the network’s proclivities toward security (dispersion) or efficiency (ease of coordination), we pose four research questions related to criminal organizations, power and coordination strategies. We find that the Mafia network is distributed as a portfolio of nearby and distant ties with significant spatial clustering among the Mafia family units.The methods used here differ from former methods that analyze the point pattern locations of individuals and the social network of individuals separately. The research techniques used here contribute to the body of non-planar network analysis methods in GIScience and can be generalized to other types of spatially-embedded social networks.

A spatial social network (SSN) can be defined as a set of agent-based connections that are embedded in geographic space. SSNs range in size from small networks of connections within a group of peop...

Social Network Analysis offers powerful tools to analyze the structure of relationships between a set of people. However, the addition of spatial information poses new challenges, as nodes are embedded simultaneously in network space and Euclidean space. While nearby nodes may not form social ties, ties may exist at a distance, a configuration ill-suited for traditional spatial metrics that assume adjacent objects are related. As such, there are relatively few metrics to describe these nuanced situations. We advance the burgeoning field of spatial social network analysis by introducing a set of new metrics. Specifically, we introduce the spatial social network schema, tuning parameter and the flattening ratio, each of which leverages the notion of ‘distance’ to augment insights obtained by relying on topology alone. These methods are used to answer the questions: What is the social and spatial structure of the network? Who are the key individuals at different spatial scales? We use two synthetic networks with properties mimicking the ones reported in the literature as validation datasets and a case study of employer–employee network. The methods characterize the employer–employee as spatially loose with predominantly local connections and identify key individuals responsible for keeping the network connected at different spatial scales.

Today, online social media outlets provide new and plentiful sources of data on social networks (SNs) and location-based social networks (LBSNs), i.e., geolocated evidence of connections between individuals. While SNs have been used to show how the magnitude of social connectivity decreases with distance, there are few examples of how to include SNs as layers in a GISystem. If SNs, and thus, interpersonal relationships, could be analyzed in a geographic information system (GIS) setting, we could better model how humans socialize, share information, and form social groups within the complex geographic landscape.Our goal is to facilitate a guide for analyzing SNs (as derived from online social media, telecommunications, surveys, etc.) within geographic space by combining the mature fields of social network analysis (SNA) and GISystems. First, we describe why modeling socialization in geographic space is essential for understanding human behavior. We then outline best practices and techniques for embedding SN nodes and edges in GISystems by introducing terms like ‘social flow’ and ‘anthrospace’, and categorizations for data and spatial aggregation types. Finally, we explore case study vignettes of SNA within GISystems from diverse regions located in Bolivia, China, Côte d’Ivoire, Singapore, the United Kingdom, and the United States, using concepts such as geolocated dyads, ego–alter relationships, node feature roles, modularity, and network transitivity.

Institutions are powerful forces in modern society -- they affect human mobility, and are constantly pushing and pulling individuals across geographic space. Institution data is rarely including in studies of mobility, although we find these data to be a rich source of information on human mobility. We visit over 1000 U.S. University athletics web pages from 78 universities, 20 different types of sports, to produce a 90,000+ record dataset of U.S. And international student athletes and their hometowns. This new agent-based origin-destination data set has not been previously gathered into one file for public use. For each university, we measure its "pull power", i.e. Its ability to attract students from many different, and distant hometowns. From these data, we explore the mobility of students from different universities and find witch institutions are catalyzing human migration and movement to 'powerful' extents.

In this paper, we propose a tool to explore human movement dynamics in a Metropolitan Area. By analyzing a mass of individual cell phone traces, we build a Human-City Interaction System for understanding urban mobility patterns at different user-controlled temporal and geographic scales. We solve the problems that are found in available tools for spatio-temporal analysis, by allowing seamless manipulability and introducing a simultaneous\multi-scale visualization of individual and aggregate flows. Our tool is built to support the exploration and discovery of urban mobility patterns and the daily interactions of millions of people. Moreover, we implement an intelligent algorithm to evaluate the level of mobility homophily of people moving from place to place.