The transition toward smart and sustainable cities in the Philippines requires both technological innovation and institutional coordination. This paper presents the integrated development and deployment of smart city platforms under three major initiatives—LUNGSOD, SMART METRO, and FASTRAC—led by the University of the Philippines Training Center for Applied Geodesy and Photogrammetry (TCAGP) and supported by the Department of Science and Technology (DOST). LUNGSOD developed city-level dashboards in Iloilo City that enabled emergency response, citizen engagement, and spatial planning through a modular WebGIS and mobile application. SMART METRO scaled this concept to a regional level, building a multi-LGU data ecosystem anchored on geospatial databases, digital twins, and regional coordination dashboards across the Metro Iloilo–Guimaras Economic Development Council (MIGEDC), Zamboanga City, and Bayugan. FASTRAC, through LUNGSOD-FASTRAC, further enhanced these systems with scalable software engineering, security upgrades, and tiered deployment packages, addressing the challenge of translating R&D outputs into operational civic technology solutions. Across these initiatives, the evolution from cities’ dashboards to interoperable civic tech platforms highlights the importance of user-centered design, open data architectures, and cross-jurisdictional coordination. The paper discusses key methodologies, pilot implementations, and governance models that enable these platforms to support responsive, data-driven governance. Lessons from these projects underscore the need for flexible procurement pathways, sustained capacity building, and alignment with national systems. Ultimately, the work demonstrates a replicable model for institutionalizing smart governance infrastructures at both city and regional levels, contributing to the Philippines’ broader digital transformation goals.

Citizen science leveraging social media-based platforms offers a powerful tool for large-scale biodiversity monitoring and public engagement. However, inherent biases related to observer behavior affect the data patterns. Understanding the drivers of observation hotspots – areas with high data density – is vital for data interpretation and project design optimization. This study investigated the factors forming citizen science biodiversity observation hotspots in the urban forests of Tsukuba Science City, Japan, hypothesizing that human accessibility factors are as important as ecological factors. We analyzed 17,174 filtered wildlife observations from the citizen science platform (2019-2024) across 54 km^2 grid squares associated with Densely Inhabited Districts. We classified forest land cover into three accessibility types (Public Wayside, Public Inland, Remote), based on road proximity and public access status. We compared MaxEnt models for the five taxonomic groups using (1) basic land cover categories and (2) land cover with refined forest accessibility categories. Model performance was evaluated using the AUC. These results strongly support our hypotheses. Models incorporating forest accessibility (Model 2) consistently outperformed basic land cover models (Model 1). In Model 2, human accessibility factors were major contributors to predicting hotspots and specific land cover factors connected to species distributions. These findings highlight the critical role of biases that arise from social variables, defined by accessibility, in driving citizen science data patterns. Accounting for accessibility is essential for interpreting citizen science data, designing effective engagement strategies, and planning biodiversity-friendly, accessible urban green spaces.