Street-Level Hotspot Mapping from Municipal Drainage Unclogging Work Orders: Evidence for Risk-Based Prioritization in Guarujá, Brazil (2021–2025)

ntroduction: Urban flooding and recurrent stormwater drainage failures are often associated with clogged storm drains and microdrainage constraints. Municipal urban operations departments routinely register unclogging work orders, creating an operational dataset that can be leveraged to detect recurrence patterns and support preventive decision-making. This study proposes a practical method to map recurrent “hotspots” using municipal unclogging work-order records in Guarujá, Brazil, covering January 2021 to December 2025.
Methods: Work orders were consolidated at the street-name (logradouro) and neighborhood levels. House-number information was intentionally excluded due to historical inconsistencies in address completion. To minimize noise from spelling variants and abbreviations, we applied text normalization and string-similarity grouping to merge equivalent street names. Hotspots were defined and ranked using recurrence metrics (work-order frequency per street) and temporal persistence indicators (reappearance across different months and years). Neighborhood-level summaries were produced to identify spatial concentration of recurrent streets.
Results: The approach generates a prioritized list of streets with the highest recurrence and persistence, distinguishing isolated episodes from chronic locations repeatedly demanding reactive maintenance. The resulting hotspot inventory can support preventive maintenance scheduling, coordinated field operations, and the identification of sites where structural interventions may be more effective than repeated reactive responses.
Conclusions: Municipal work-order records can function as an administrative “sensor network” to detect street-level drainage hotspots and inform evidence-based prioritization for urban resilience and climate adaptation. The method is low-cost, scalable, and transferable to other cities facing recurring flooding and drainage maintenance burdens.