This study assesses the impact of urban land use on the climatological distribution of thunderstorm initiation occurrences in the humid subtropical region of the southeast United States, which includes the Atlanta, Georgia metropolitan area. Initially, an automated technique is developed to extract the locations of isolated convective initiation (ICI) events from 17 years (1997–2013) of composite reflectivity radar data for the study area. Nearly 26 000 ICI points were detected during 85 warm‐season months, providing the foundation for first long‐term, systematic assessment of the influence of urban land use on thunderstorm development. Results reveal that ICI events occur more often over the urban area compared to its surrounding rural counterparts, confirming that anthropogenic‐induced changes in land cover in moist tropical environments lead to more initiation events, resulting thunderstorms and affiliated hazards over the developed area. The ICI risk for Atlanta is greatest during the late afternoon and early evening in July and August in synoptically benign conditions. Greater ICI counts downwind of Atlanta suggest that prevailing wind direction also influences the location of these events. Moreover, ICI occurrences over the city were significantly higher on weekdays compared to weekend days—a result that was not apparent in a rural control region located west of the city. This suggests that the weekly commuting cycle and associated aerosol levels of Atlanta may amplify ICI rates. The investigation provides a methodological framework for future studies that examine the effect of land use, land cover, and terrain discontinuities on the spatio‐temporal character of ICI events.

Slope exerts a powerful influence on the route selection processes of humans. Attempts to model human movement in hilly and mountainous terrain which have largely focused on least-time route transformations can be improved by incorporating research suggesting that humans systematically overestimate slopes. Such research suggests that cost functions derived from slope should be more expensive than time derivations alone would indicate. This paper presents a method for empirically estimating cost functions for slopes. We use the method to predict routes and paths that are more likely to be selected by humans based on their perceptions of slope. An evaluation of the method found that it successfully predicts road, track and trail locations over a variety of conditions and distances.