Urban heat, a significant environmental hazard, poses challenges to urban sustainability and public health. Existing research often overlooks the influence of human mobility networks on the spatial distribution and impact of urban heat. This study aims to bridge this gap by examining the interplay between human mobility and urban heat exposure. The research focuses on three key properties emerging from this intersection: (1) heat traps, where populations in high-heat areas primarily visit other high-heat areas; (2) heat escapes, where populations in high-heat areas visit low-heat areas; and (3) heat escalates, where populations in low-heat areas visit high-heat areas. The study utilizes aggregated and anonymized location-based data from twenty US metropolitan areas to construct human mobility networks and analyze the prevalence of these three properties across different cities. The study's objective is to quantify the extent to which human mobility exacerbates, alleviates, or expands the reach of urban heat exposure.

Previous research has explored urban heat island effects, identifying factors such as tree density, transportation patterns, and population density as contributing elements. However, these studies often focus on individual factors and neglect the dynamic aspect of human mobility in shaping heat exposure. While some studies have examined the interaction of mobility with other hazards like air pollution and infectious diseases, the relationship between human mobility and urban heat remains largely under-investigated. Existing approaches, such as dynamic urban thermal exposure indices, account for mobility but don't fully capture the emergent properties (heat traps, escapes, and escalates) arising from the interaction of mobility networks and heat distribution. This study addresses this knowledge gap by defining and analyzing these emergent properties.

This study uses data from February 2020, prior to the COVID-19 pandemic, to represent typical mobility patterns. Urban heat exposure data were obtained from the United States Surface Urban Heat Island (SUHI) database, utilizing annual mean SUHI values. SUHI, while acknowledging its limitations in directly measuring human-perceived heat, serves as a proxy for spatial heat distribution. Location-based data were obtained from Spectus, which provides anonymized and aggregated data on device locations and dwell times. The data processing involved identifying each device's home census tract based on dwell times and constructing a mobility network representing trips between census tracts. Census tracts were categorized into high and low urban heat (UH) areas using quantile breaks. Three ratios were calculated for each tract: (1) the ratio of trips from low UH areas to high UH areas (heat escalates), (2) the ratio of trips from high UH areas to low UH areas (heat escapes), and (3) the ratio of trips from high UH areas to other high UH areas (heat traps). Cities were classified based on the dominant trip category (more than half of the trips).

The analysis revealed significant variations in urban heat patterns across the 20 cities. Los Angeles and Chicago exhibited strong urban heat traps, with a large percentage of trips originating from high-heat areas remaining within high-heat areas. For instance, in Los Angeles, 81% of tracts in high UH areas showed trips trapped within high UH areas. In contrast, cities like Boston and Atlanta showed lower percentages of heat traps. Minneapolis and Dallas demonstrated higher ratios of heat escapes, with a notable proportion of trips moving from high-heat to low-heat areas. The spatial distribution of heat traps and escapes also varied across cities. For example, heat traps in Chicago were more clustered, while those in Los Angeles were distributed in multiple clusters. The study found that heat escapes were less common than heat traps, and heat escalates, while present, were also comparatively less prevalent across the selected cities. No statistically significant relationship was found between the presence of heat traps and demographic segregation factors, suggesting that heat traps are emergent properties not solely attributable to demographic or facility-related factors.

The findings highlight the importance of considering human mobility patterns when assessing and mitigating urban heat exposure. The emergence of urban heat traps suggests that urban design strategies should focus on creating more pathways for residents of high-heat areas to access cooler areas. Conversely, cities with significant heat escapes may benefit from interventions that enhance the resilience and comfort of low-heat areas. The study’s novel classification of heat traps, escapes, and escalates offers valuable metrics for evaluating urban heat dynamics and guiding targeted interventions. While the current study uses SUHI as a proxy, future work could incorporate other heat exposure metrics (air temperature, humidity) for a more comprehensive assessment. Understanding mobility-induced heat escalation in low-heat areas is crucial for developing strategies to reduce overall heat-related risks for all populations. The lack of strong correlation between heat traps and socio-demographic factors warrants further research to fully understand the underlying mechanisms.

This study provides a novel perspective on urban heat exposure by integrating human mobility data. The identification of urban heat traps, escapes, and escalates offers valuable insights for urban planning and design. Future research should explore the influence of different temporal scales, incorporate finer-grained data on microclimates and individual heat exposure, and investigate the socio-economic implications of these spatial patterns. The interplay between urban heat and mobility presents complex challenges requiring a multi-faceted approach to urban planning.

The study's limitations include reliance on smartphone data, which may not represent the entire population equally. The lack of information on visit duration and indoor conditions can affect the interpretation of trip purposes and actual heat exposure. The mobility data does not include cross-county trips. Further, while the study considers the annual mean SUHI, a more in-depth analysis of seasonal variation and daytime versus nighttime temperatures could provide a more complete picture.