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In this study, we investigate the compatibility of specific vulnerability indicators and heat exposure data and the suitability of spatial temperature-related data at a range of resolutions, to represent spatial temperature variations within cities using data from Atlanta, Georgia. For this purpose, we include various types of known and theoretically based vulnerability indicators such as specific street-level landscape features and urban form metrics, population-based and zone-based variables as predictors, and different measures of temperature, including air temperature (as vector-based data), land surface temperature (at resolution ranges from 30 m to 305 m), and mean radiant temperature (at resolution ranges from 1 m to 39 m) as dependent variables. Using regression analysis, we examine how different sets of predictors and spatial resolutions can explain spatial heat variation. Our findings suggest that the lower resolution of land surface temperature data, up to 152 m, and mean radiant temperature data, up to 15 m, may still satisfactorily represent spatial urban temperature variation caused by landscape elements. The results of this study have important implications for heat-related policies and planning by providing insights into the appropriate sets of data and relevant resolution of temperature measurements for representing spatial urban heat variations.