All hubs actively used geospatial tools for data generation, including

• Kobo Toolbox (Africa hub)
• Cities Navigator (Africa hub)
• Microsoft Power BI (Latin America hub)
• QGIS (Asia Hub)
• Common to all projects was the use of ArcGIS, Python, and R to conduct customized analysis and visualizations.

The C2M2 program generated diverse data, including:

• real-time crime spatiotemporal data
• water and sanitation data
• migration data
• health facility data, and
• tourism data

Important lessons learned include:

1. real-time crime spatiotemporal data
2. Identification and collection of new georeferenced data at the sub-city scale (i.e., city districts or neighborhoods). Many of the city projects collected data that had never before been collected, providing new insights for city planning and decision making (i.e., locations of all types of healthcare facilities such as clinics, hospitals, pharmacies)
3. The use of geospatial tools, common methods, and standardized data structure (i.e., attributes, units, categories) enable robust analyses across thematic issues. C2M2 projects examined different second-order impacts using common geospatial approaches.
4. Existing data could be enhanced by collecting additional attribute information. For example, characteristics of water points (i.e., private/public, hours of operation) and toilets (i.e., handwashing facilities at location) provided additional information relevant to government policies to manage the pandemic.
5. Demonstrating the integration of different methods. Projects used mobile tools to conduct surveys with sample populations with georeferenced locations.
6. Protection of personal information and informed consent. Ethics underpin surveys and georeferenced data collection. Georeferenced data reveal sensitive information regarding location and people. Protection of personal information is necessary for data generation and sharing. Ensuring participants understood informed consent while protecting anonymity was essential to all projects.
7. Analysis and visualization of geospatial data for decision making. Data can provide the locational analysis to identify place-based solutions (i.e., density analysis of point data to yield “heat maps” of sensitive areas). However, stakeholders and decision-makers need to be aware of the utility of a geospatial approach. Visualizations (data dashboards) and storytelling (Esri Story Maps) are means to demonstrate data results.