arXiv subjects

Clio Andris

Publications and source records attributed to Clio Andris.

7 recordsLinked to original sources

arXiv2026

A Needs Assessment for Measuring Geographic - Legislative Associations in the U.S. House of Representatives

Political legislation affects the well-being and livelihoods of constituents. In the U.S. Congress a representative's voting record on bills and legislation is public. These bills have themes associated with them, such as veterans' affairs, coastal monitoring, agricultural appropriations, etc. A bill on veterans' affairs may affect a constituency differently if they have a high percentage of veterans. In this work, we demonstrate how congressional vote outcomes can be merged with typical geographic information systems (GIS) data to help compare a legislator's votes with the geographies of their constituencies to measure the association between district features and legislators' decisions. We retrieved and tagged bills from the 118th U.S. House of Representatives (Jan. 2023 - Jan. 2025) by manually assigning each bill a set of themes. We then retrieved spatial data at the congressional district level related to each theme. We built a backend database to connect bill information and spatial data, and an interactive map prototype displayed the spatial data related to each theme associated with each bill. Our work helps identify challenges to creating a more accessible and complete system that would facilitate the visualization and analysis of the nature of contemporary Congressional representation. This technical report is primarily written for members of the data science community, and specifically those interested in legislative and geographic data.

cs.HC↗

arXiv2025

Exploropleth: exploratory analysis of data binning methods in choropleth maps

When creating choropleth maps, mapmakers often bin (i.e., group, classify) quantitative data values into groups to help show that certain areas fall within a similar range of values. For instance, a mapmaker may divide counties into groups of high, middle, and low life expectancy (measured in years). It is well known that different binning methods (e.g., natural breaks, quantile) yield different groupings, meaning the same data can be presented differently depending on how it is divided into bins. To help guide a wide variety of users, we present a new, open source, web-based, geospatial visualization tool, Exploropleth, that lets users interact with a catalog of established data binning methods, and subsequently compare, customize, and export custom maps. This tool advances the state of the art by providing multiple binning methods in one view and supporting administrative unit reclassification on-the-fly. We interviewed 16 cartographers and geographic information systems (GIS) experts from 13 government organizations, non-government organizations (NGOs), and federal agencies who identified opportunities to integrate Exploropleth into their existing mapmaking workflow, and found that the tool has potential to educate students as well as mapmakers with varying levels of experience. Exploropleth is open-source and publicly available at https://exploropleth.github.io.

cs.HC↗

arXiv2025

Mapping a Movement: Exploring a Proposed Police Training Facility in Atlanta and the Stop Cop City Movement through Online Maps

In 2021, the City of Atlanta and Atlanta Police Foundation launched plans to build a large police training facility in the South River Forest in unincorporated DeKalb County, GA. 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. Here, 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. 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 and roles that maps and mapping technologies can play in social movements. We examine the extent to which these maps provide data to confirm or contradict concerns raised by grassroots organizations and local residents about the facility. We find that stakeholders and mapmakers use geospatial tools in different ways and likely have varied access to mapping technologies. We argue that documenting the use of maps to communicate information about a contentious project can help enumerate community positions and perspectives, and we 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.

cs.HC↗

arXiv2025

Cartographers in Cubicles: How Training and Preferences of Mapmakers Interplay with Structures and Norms in Not-for-Profit Organizations

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.

cs.HC↗

arXiv2024

Lessons from a human-in-the-loop machine learning approach for identifying vacant, abandoned, and deteriorated properties in Savannah, Georgia

Addressing strategies for managing vacant, abandoned, and deteriorated (VAD) properties is important for maintaining healthy communities. Yet, the process of identifying these properties can be difficult. Here, we create a human-in-the-loop machine learning (HITLML) model called VADecide and apply it to a parcel-level case study in Savannah, Georgia. The results show a higher prediction accuracy than was achieved when using a machine learning model without human input in the training. The HITLML approach also reveals differences between machine vs. human-generated results. Our findings contribute to knowledge about the advantages and challenges of HITLML in urban planning. [Accepted for Publication at a Peer Review Journal]

cs.LG↗

arXiv2022

Visualization Design Practices in a Crisis: Behind the Scenes with COVID-19 Dashboard Creators

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. 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.

cs.HC↗

arXiv2020

Spatial Social Network (SSN) Hot Spot Detection: Scan Methods for Non-Planar Networks

Moving window and hot spot detection analyses are statistical methods used to analyze point patterns within a given area. Such methods have been used to successfully detect clusters of point events such as car thefts or incidences of cancer. Yet, these methods do not account for the connections between individual events, such as social ties within a neighborhood. This paper presents two GIS methods, EdgeScan and NDScan, for capturing areas with high and low levels of local social connections. Both methods are moving window processes that count the number of edges and network density, respectively, in a given focal area (window area). The focal window attaches resultant EdgeScan and NDScan statistics to nodes at the center of the focal window area. We implement these methods on a case study of 1960s connections between members of the Mafia in New York City. We use various definitions of a focal neighborhood including Euclidean, Manhattan and K Nearest Neighbor (KNN) definitions. We find that KNN tends to overstate the values of local networks, and that there is more variation in outcome values for nodes on the periphery of the study area. We find that, location-wise, EdgeScan and NDScan hot spots differ from traditional spatial hot spots in the study area. These methods can be extended to future studies that detect local triads and motifs, which can capture the local network structure in more detail.

cs.SI↗