Community Engagement

Effective urban planning, and in particular, planning for climate change impacts, adaptation, and mitigation, requires a democratic, collaborative approach that engages community stakeholders with leaders and policymakers. It is imperative that formal policy instruments and strategies be in-step with more informal institutions, such as community members, as well as with formal actors in the environmental, economic, and sociopolitical realms. Such participatory methods are often critical in identifying existing structural inequalities and in gaining institutional support for a planning process that is dynamically working with local actors and agents.

There are obstacles to creating effective participatory planning, especially with so-called "wicked problems" such as sea level rise, which are not easily solved with existing or traditional strategies. Further, attempts to increase and improve public participation are often focused on simply increasing the number of citizens that are engaged, rather than improving the quality of that engagement, and thus the quality of the results obtained. With the help of an undergraduate student we have performed a social network analysis to identify the SLR stakeholder map of Tampa Bay (see Figure below).

In addition to gaming (see Thrust 5), we will engage with the community in several ways.

Scenario Planning Reports: We will use the computational model for scenario planning (see Thrust 1) to generate specific reports for interested stakeholders. To that end we will develop specific detailed models by focusing on the stakeholder’s interest areas, action sets, and interactions with other stakeholders. For instance, an environmental stakeholder (e.g., Audubon Society) may be most interested in understanding the impacts of sea level rise on natural ecosystems (e.g., bird habitats). In another example, focusing on a city government we can provide scenario simulations to the Tampa Bay Climate Science Advisory Panel, in which PI Hafen serves as a member. Finally, an insurance company may be interested in such a scenario simulation study to asses the risks of SLR to help in setting insurance premiums in a coastal city, such as Dunedin.

Visualization: Our interactive visualizations will be made available through our aforementioned app and project website in Thrust 2. This will include general content, such as an engaging virtual fly-through of the Tampa Bay area, viewable through our custom-printed Google Cardboard units. Specific content will be tailored to each group and stakeholder, translating our relevant data into a user-friendly narrative to best visually communicates the particular environmental, societal, and economic impacts of SLR. For example, for the Audubon Society, this could include an immersive VR simulation of a temporal scenario that also includes spatially-specific auditory content – such as the declining sounds of local bird calls due to habitat loss from SLR. Broad dissemination will be fostered through multi-platform social media campaigns for disseminating project information and content, as well as to promote community engagement and connectedness; future integration will be enhanced through Facebook Spaces VR app (and/or the Oculus Rift headset).

DCPR: For the purposes of our project, a Digital Common Property Resource (DCPR) is a storehouse of all existing information relevant to SLR and its impacts in Tampa Bay, made useful to researchers and stakeholders through the interface of a computational model. Our application of a DCPR has four elements:

1. Datasets, from a number of different dimensions of the socio-ecological system that is Tampa Bay. This data will be aggregated from a range of sources and types. For example, existing spatial data on physical vulnerability of coastal properties such as Federal Emergency Management Agency (FEMA) repetitive loss maps, university research from sources such as the Water Institute and the Coastal Ocean Monitoring and Prediction System, specific issue datasets from non-profit advocacy organizations such as bird sighting data from the Audubon Society, and proprietary datasets such as repetitive loss maps from FEMA, demographic and economic data from the Hillsborough County Planning Commission, Geographic Information Systems (GIS) data from the Water Institute. This data will be housed within the USF library in their existing geospatial data infrastructure system.
2. A computational model that relates heterogeneous data in ways that make it useful to generate scenarios. Please refer to Thrust 1 for more detail.
3. Findings, generated by the core team and other researchers. Findings incorporated in the DCPR can take three forms:
   • datasets of future scenarios, produced by the computational model,
   • visualizations of that data, and
   • publications related to SLR and/or referencing the model and datasets in the DCPR.
4. A web interface that makes these elements available to other researchers, stakeholders to Tampa Bay, and the general public. This will be housed within the website for the USF Library.