Integrating human mobility and animal movement data reveals complex space-use between humans and white-tailed deer in urban environments

Abstract

Human expansion into wildlife habitats has increased the need to understand human-wildlife interactions, necessitating interdisciplinary approaches to assess zoonotic disease transmission risks and public health impacts. This study integrated fine-grained human foot traffic data with hourly GPS data from 38 white-tailed deer (Odocoileus virginianus), a species linked to SARS-CoV-2, brucella, and chronic wasting disease, in Howard County, Maryland. We explored spatial and temporal overlap between human and deer activity over 24 months (2018-2019) across a hexagonal tessellation with metrics like hourly popularity and visit counts. Negative binomial models were fitted to the visit counts of each deer and humans per tessellation area, using landscape features as predictors. A separate deer-only model included commercial human activity as another predictor. Spatial analysis showed deer and humans sharing spaces in the study area, with results indicating deer using more populated residential areas and areas with commercial activity. Temporal analysis showed deer avoiding commercial spaces during daytime but using them in late evening and early morning. These findings highlight the complex space use between species and the importance of integrating detailed human mobility and animal movement data when managing wildlife-human conflict and zoonotic disease transmission, particularly in urban areas with a high probability of deer-human interactions.

Keywords: Data integration; Human mobility; Human–wildlife interactions; Movement ecology.

Fig. 1

Study area in Howard County, Maryland, defined by a hexagonal tessellation (tan hexagons) within the 100% MCP of deer home ranges, showing commercial POIs (orange points), residential building footprints (brown polygons), and county parks (green polygons). Hexagons within each Howard County park were merged into single polygons. Map was created using ArcGIS Pro 3.3.2 (basemap source: County of Anne Arundel, VGIN, Esri, TomTom, Garmin, SafeGraph, FAO, METI/NASA, USGS, EPA, NPS, USFWS, USDA, M-NCPPC, NOAA).

Fig. 2

Mean visit counts of deer (blue) and humans (orange) in the hexagonal tessellation from January 2018 to December 2019, with hexagons within each Howard County park merged into larger park polygons (a–f). Monthly mean visit counts of deer (a) and humans (b). Winter mean visit counts of deer (c) and humans (d). Summer mean visit counts of deer (e) and humans (f). Maps were created using ArcGIS Pro 3.3.2 (basemap source: County of Anne Arundel, VGIN, Esri, TomTom, Garmin, SafeGraph, FAO, METI/NASA, USGS, EPA, NPS, USFWS).

Fig. 3

Mean popularity by hour of deer (blue line) and humans (orange line) in parks (a), areas with commercial activity (b), and areas without commercial activity (c) from January 2018 to December 2019. The values were smoothed using a 3-h rolling mean.

Fig. 4

Mean popularity by hour of deer (blue line) and humans (orange line) during winter and summer in parks (a,b), areas with commercial activity (c,d), and areas without commercial activity (e,f) from January 2018 to December 2019. The values were smoothed using a 3-hour rolling mean.

Fig. 5

Posterior distributions of estimated model coefficients from models predicting deer (blue) (a,b) and human (orange) (b) activity. Outlined distributions represent all values in the posterior distribution and colored areas represent the 95% credible intervals.

Fig. 6

Visualization of the hexagonal tessellation showing POIs within a hexagon (a) and deer movement trajectories across the tessellation (b).