@inproceedings{white_2023, title={A Web-Based Platform to Democratize Geospatial Participatory Modeling in the Cloud}, url={https://zenodo.org/doi/10.5281/zenodo.10456565}, DOI={10.5281/ZENODO.10456565}, booktitle={Zenodo}, publisher={Zenodo}, author={White, Corey}, year={2023}, month={Dec} }
 @article{white_petrasova_petras_tateosian_vukomanovic_mitasova_meentemeyer_2023, title={An open-source platform for geospatial participatory modeling in the cloud}, volume={167}, ISSN={["1873-6726"]}, url={https://doi.org/10.1016/j.envsoft.2023.105767}, DOI={10.1016/j.envsoft.2023.105767}, abstractNote={Participatory modeling facilitates the co-production of knowledge and action by engaging stakeholders in research. However, the spatial dimensions of socio-environmental systems and decision-making are challenging to incorporate in participatory models, as developing interactive geospatial models requires specialized knowledge. Yet, many of society's most pressing and complex socio-environmental problems require participatory modeling that is geospatial. Existing interactive online applications have broadened the audiences who can engage with geospatial models, but often do not provide a robust framework for interactive model development. Here, we develop an open-source platform, OpenPlains, to address barriers to participation in geospatial modeling by enabling researchers to develop interactive models that remove barriers to data aggregation and user engagement. OpenPlains consists of six new open-source libraries: OpenPlains, django-actina, grass-js-client, react-openplains, react-ol, and openplains-cli. We demonstrate OpenPlains through two web applications that work anywhere in the contiguous United States: a spatial–temporal watershed analysis application and an urban growth forecasting application.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, publisher={Elsevier BV}, author={White, Corey T. and Petrasova, Anna and Petras, Vaclav and Tateosian, Laura G. and Vukomanovic, Jelena and Mitasova, Helena and Meentemeyer, Ross K.}, year={2023}, month={Sep} }
 @article{white_white_2023, title={tomorrownow/TomorrowNowApp: v0.0.1 (alpha.0)}, url={https://zenodo.org/record/7702537}, DOI={10.5281/ZENODO.7702537}, journal={Zenodo}, publisher={Zenodo}, author={White, Corey and White, Corey T.}, editor={White, Corey T.Editor}, year={2023}, month={Mar} }
 @article{white_2023, title={tomorrownow/grass-stac-extension: v1.0.0}, url={https://zenodo.org/record/7703038}, DOI={10.5281/ZENODO.7703038}, journal={Zenodo}, publisher={Zenodo}, author={White, Corey}, year={2023}, month={Mar} }
 @article{white_reckling_petrasova_meentemeyer_mitasova_2022, title={Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion}, volume={14}, ISSN={["2072-4292"]}, url={https://www.mdpi.com/2072-4292/14/7/1718}, DOI={10.3390/rs14071718}, abstractNote={As rapid urbanization occurs in cities worldwide, the importance of maintaining updated digital elevation models (DEM) will continue to increase. However, due to the cost of generating high-resolution DEM over large spatial extents, the temporal resolution of DEMs is coarse in many regions. Low-cost unmanned aerial vehicles (UAS) and DEM data fusion provide a partial solution to improving the temporal resolution of DEM but do not identify which areas of a DEM require updates. We present Rapid-DEM, a framework that identifies and prioritizes locations with a high likelihood of an urban topographic change to target UAS data acquisition and fusion to provide up-to-date DEM. The framework uses PlanetScope 3 m satellite imagery, Google Earth Engine, and OpenStreetMap for land cover classification. GRASS GIS generates a contextualized priority queue from the land cover data and outputs polygons for UAS flight planning. Low-cost UAS fly the identified areas, and WebODM generates a DEM from the UAS survey data. The UAS data is fused with an existing DEM and uploaded to a public data repository. To demonstrate Rapid-DEM a case study in the Walnut Creek Watershed in Wake County, North Carolina is presented. Two land cover classification models were generated using random forests with an overall accuracy of 89% (kappa 0.86) and 91% (kappa 0.88). The priority queue identified 109 priority locations representing 1.5% area of the watershed. Large forest clearings were the highest priority locations, followed by newly constructed buildings. The highest priority site was a 0.5 km2 forest clearing that was mapped with UAS, generating a 15 cm DEM. The UAS DEM was resampled to 3 m resolution and fused with USGS NED 1/9 arc-second DEM data. Surface water flow was simulated over the original and updated DEM to illustrate the impact of the topographic change on flow patterns and highlight the importance of timely DEM updates.}, number={7}, journal={REMOTE SENSING}, publisher={MDPI AG}, author={White, Corey T. and Reckling, William and Petrasova, Anna and Meentemeyer, Ross K. and Mitasova, Helena}, year={2022}, month={Apr} }
 @article{white_mitasova_bendor_foy_pala_vukomanovic_meentemeyer_2021, title={Spatially Explicit Fuzzy Cognitive Mapping for Participatory Modeling of Stormwater Management}, volume={10}, ISSN={["2073-445X"]}, url={https://doi.org/10.3390/land10111114}, DOI={10.3390/land10111114}, abstractNote={Addressing “wicked” problems like urban stormwater management necessitates building shared understanding among diverse stakeholders with the influence to enact solutions cooperatively. Fuzzy cognitive maps (FCMs) are participatory modeling tools that enable diverse stakeholders to articulate the components of a socio-environmental system (SES) and describe their interactions. However, the spatial scale of an FCM is rarely explicitly considered, despite the influence of spatial scale on SES. We developed a technique to couple FCMs with spatially explicit survey data to connect stakeholder conceptualization of urban stormwater management at a regional scale with specific stormwater problems they identified. We used geospatial data and flooding simulation models to quantitatively evaluate stakeholders’ descriptions of location-specific problems. We found that stakeholders used a wide variety of language to describe variables in their FCMs and that government and academic stakeholders used significantly different suites of variables. We also found that regional FCM did not downscale well to concerns at finer spatial scales; variables and causal relationships important at location-specific scales were often different or missing from the regional FCM. This study demonstrates the spatial framing of stormwater problems influences the perceived range of possible problems, barriers, and solutions through spatial cognitive filtering of the system’s boundaries.}, number={11}, journal={LAND}, publisher={MDPI AG}, author={White, Corey T. and Mitasova, Helena and BenDor, Todd K. and Foy, Kevin and Pala, Okan and Vukomanovic, Jelena and Meentemeyer, Ross K.}, year={2021}, month={Nov} }