Cities shape mosquito and arboviral risk through the way heat, water, vegetation, and drainage are managed. We present a fully reproducible early-warning pipeline that predicts trap–week West Nile virus (WNV) positivity one to two weeks ahead using programmatically accessible sources (Socrata WNV pools and Meteostat weather), and a graph-aware post-hoc smoothing that enforces spatial coherence of predicted risks. Because direct API access to the Chicago portal requires a Socrata App Token, all reported experiments use an offline, synthetic trap–week panel that mirrors a Chicago-like spatio-seasonal structure, while we ship turn-key code to re-run the identical analysis on the real data once a token is provided. Across forward-chaining evaluations, gradient boosting with Laplacian smoothing delivers discrimination comparable to that of a strong elastic-net baseline, as assessed by the area under the receiver operating characteristic curve, but substantially better probabilistic calibration (lower Brier and improved reliability). Feature profiles emphasize antecedent heat and relative dryness, aligning with ecological priors, and enable tiered, probability-based operational playbooks for vector control and sustainability co-actions (cooling, drainage, vegetation). The pipeline is designed for transparency, portability, and policy relevance: calibrated probabilities support graded interventions and top-K targeting under budgets, while code parity between synthetic and real modes facilitates external replication.