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Environmental hazards vary locally and even street to street resulting in microspatial inequities, necessitating climate resilience solutions that respond to specific hyperlocal conditions. This study uses remote sensing data to estimate two environmental hazards that are particularly relevant to community health: land surface temperature (LST; from LandSat) and air pollution (AP; from motor vehicle volume via cell phone records). These data are analyzed in conjunction with land use records in Boston, MA to test (1) the extent to which each hazard concentrates on specific streets within neighborhoods, (2) the infrastructural elements that drive variation in the hazards, and (3) how strongly hazards overlap in space. Though these data rely on proxies, they provide preliminary evidence. Substantial variations in LST and AP existed between streets in the same neighborhood (40% and 70–80% of variance, respectively). The former were driven by canopy, impervious surfaces, and albedo. The latter were associated with main streets and zoning with tall buildings. The correlation between LST and AP was moderate across census tracts (r = 0.4) but modest across streets within census tracts (r = 0.16). The combination of results confirms not only the presence of microspatial inequities for both hazards but also their limited coincidence, indicating that some streets suffer from both hazards, some from neither, and others from only one. There is a need for more precise, temporally-dynamic data tracking environmental hazards (e.g., from environmental sensor networks) and strategies for translating them into community-based solutions.