Accelerating ethics, empathy, and equity in geographic information science

Exploring geomasking methods for geoprivacy: a pilot study in an environment with built features

This study discusses the ethical use of geographical information systems (GIS) data with a focus on geomasking for upholding locational privacy. As part of a pilot study in Jeddah City, Saudi Arabia, we used open-source geomasking methods to ensure geoprivacy while examining built environment features that determine the quality of life among individuals with type-II diabetes. We employed the open-source algorithms Maskmy.XYZ and NRand-k for geomasking 329 data points. The results showed no differences between global and city-level spatial patterns, but significant variations were observed with respect to local patterns. These findings indicate the promising potential of the chosen geomasking technologies with respect to ensuring locational privacy but it was noted that further improvements are needed. We recommend developing enhanced algorithms and conducting additional studies to minimize any negative impact of geomasking in spatial analysis with the overall aim of achieving a better understanding of ethical considerations in GIS sciences. In conclusion, application of geomasking is straightforward and can lead to enhanced use for privacy protection in geospatial data analysis.

A framework for modern time geography: emphasizing diverse constraints on accessibility

Time geography is widely used by geographers as a model for understanding accessibility. Recent changes in how access is created, an increasing awareness of the need to better understand individual variability in access, and growing availability of detailed spatial and mobility data have created an opportunity to build more flexible time geography models. Our goal is to outline a research agenda for a modern time geography that allows new modes of access and a variety of data to flexibly represent the complexity of the relationship between time and access. A modern time geography is more able to nuance individual experience and creates a pathway for monitoring progress toward inclusion. We lean on the original work by Hägerstrand and the field of movement GIScience to develop both a framework and research roadmap that, if addressed, can enhance the flexibility of time geography to help ensure time geography will continue as a cornerstone of accessibility research. The proposed framework emphasizes the individual and differentiates access based on how individuals experience internal, external, and structural factors. To enhance nuanced representation of inclusion and exclusion, we propose research needs, focusing efforts on implementing flexible space-time constraints, inclusion of definitive variables, addressing mechanisms for representing and including relative variables, and addressing the need to link between individual and population scales of analysis. The accelerated digitalization of society, including availability of new forms of digital spatial data, combined with a focus on understanding how access varies across race, income, sexual identity, and physical limitations requires new consideration for how we include constraints in our studies of access. It is an exciting era for time geography and there are massive opportunities for all geographers to consider how to incorporate new realities and research priorities into time geography models, which have had a long tradition of supporting theory and implementation of accessibility research.

Riding through the pandemic: Using Strava data to monitor the impacts of COVID-19 on spatial patterns of bicycling

COVID-19 prompted a bike boom and cities around the world responded to increased demand for space to ride with street reallocations. Evaluating these interventions has been limited by a lack of spatially-temporally continuous ridership data. Our paper aims to address this gap using crowdsourced data on bicycle ridership. We evaluate changes in spatial patterns of bicycling during the first wave of the COVID-19 pandemic (Apr - Oct 2020) in Vancouver, Canada using Strava data and a local indicator of spatial autocorrelation. We map statistically significant change in ridership and reference clusters of change to a high-resolution base map. Amongst streets where bicycling increased, we measured the proportion of increase occurring on pre-existing bicycle facilities or street reallocations compared to streets without. In all our analyses, we evaluate patterns across subsets of Strava data representing recreation, commuting, and ridership generated by women and older adults (55 + ). We found consistent and unique patterns by trip purpose and demographics: samples generated by women and older adults showed increases near green and blue spaces and on street reallocations that increased access to parks, and these patterns were also mirrored in the recreation sample. Commute ridership highlighted distinct patterns of increase around the hospital district. Across all subsets most increases occurred on bicycle facilities (pre-existing or provisional), with a strong preference for high-comfort facilities. We demonstrate that changes in spatial patterns of bicycle ridership can be monitored using Strava data, and that nuanced patterns can be identified using trip and demographic labels in the data.