@article{sanchez_petrasova_skrip_collins_lawrimore_vogler_terando_vukomanovic_mitasova_meentemeyer_2023, title={Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk}, volume={13}, ISSN={["2045-2322"]}, url={http://dx.doi.org/10.1038/s41598-023-46195-9}, DOI={10.1038/s41598-023-46195-9}, abstractNote={Abstract Impacts of sea level rise will last for centuries; therefore, flood risk modeling must transition from identifying risky locations to assessing how populations can best cope. We present the first spatially interactive (i.e., what happens at one location affects another) land change model (FUTURES 3.0) that can probabilistically predict urban growth while simulating human migration and other responses to flooding, essentially depicting the geography of impact and response. Accounting for human migration reduced total amounts of projected developed land exposed to flooding by 2050 by 5%–24%, depending on flood hazard zone (50%–0.2% annual probability). We simulated various “what-if” scenarios and found managed retreat to be the only intervention with predicted exposure below baseline conditions. In the business-as-usual scenario, existing and future development must be either protected or abandoned to cope with future flooding. Our open framework can be applied to different regions and advances local to regional-scale efforts to evaluate potential risks and tradeoffs.}, number={1}, journal={SCIENTIFIC REPORTS}, publisher={Springer Science and Business Media LLC}, author={Sanchez, Georgina M. and Petrasova, Anna and Skrip, Megan M. and Collins, Elyssa L. and Lawrimore, Margaret A. and Vogler, John B. and Terando, Adam and Vukomanovic, Jelena and Mitasova, Helena and Meentemeyer, Ross K.}, year={2023}, month={Nov} }
 @article{collins_sanchez_terando_stillwell_mitasova_sebastian_meentemeyer_2022, title={Predicting flood damage probability across the conterminous United States}, volume={17}, ISSN={["1748-9326"]}, url={https://doi.org/10.1088/1748-9326/ac4f0f}, DOI={10.1088/1748-9326/ac4f0f}, abstractNote={Floods are the leading cause of natural disaster damages in the United States, with billions of dollars incurred every year in the form of government payouts, property damages, and agricultural losses. The Federal Emergency Management Agency oversees the delineation of floodplains to mitigate damages, but disparities exist between locations designated as high risk and where flood damages occur due to land use and climate changes and incomplete floodplain mapping. We harnessed publicly available geospatial datasets and random forest algorithms to analyze the spatial distribution and underlying drivers of flood damage probability (FDP) caused by excessive rainfall and overflowing water bodies across the conterminous United States. From this, we produced the first spatially complete map of FDP for the nation, along with spatially explicit standard errors for four selected cities. We trained models using the locations of historical reported flood damage events (n = 71 434) and a suite of geospatial predictors (e.g. flood severity, climate, socio-economic exposure, topographic variables, soil properties, and hydrologic characteristics). We developed independent models for each hydrologic unit code level 2 watershed and generated a FDP for each 100 m pixel. Our model classified damage or no damage with an average area under the curve accuracy of 0.75; however, model performance varied by environmental conditions, with certain land cover classes (e.g. forest) resulting in higher error rates than others (e.g. wetlands). Our results identified FDP hotspots across multiple spatial and regional scales, with high probabilities common in both inland and coastal regions. The highest flood damage probabilities tended to be in areas of low elevation, in close proximity to streams, with extreme precipitation, and with high urban road density. Given rapid environmental changes, our study demonstrates an efficient approach for updating FDP estimates across the nation.}, number={3}, journal={ENVIRONMENTAL RESEARCH LETTERS}, author={Collins, Elyssa L. and Sanchez, Georgina M. and Terando, Adam and Stillwell, Charles C. and Mitasova, Helena and Sebastian, Antonia and Meentemeyer, Ross K.}, year={2022}, month={Mar} }
 @article{lewis_rollinson_allyn_ashander_brodie_brookson_collins_dietze_gallinat_juvigny-khenafou_et al._2022, title={The power of forecasts to advance ecological theory}, ISSN={["2041-2096"]}, DOI={10.1111/2041-210X.13955}, abstractNote={Ecological forecasting provides a powerful set of methods for predicting short‐ and long‐term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized. Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis. Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales. We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight.}, journal={METHODS IN ECOLOGY AND EVOLUTION}, author={Lewis, Abigail S. L. and Rollinson, Christine R. and Allyn, Andrew J. and Ashander, Jaime and Brodie, Stephanie and Brookson, Cole B. and Collins, Elyssa and Dietze, Michael C. and Gallinat, Amanda S. and Juvigny-Khenafou, Noel and et al.}, year={2022}, month={Aug} }
 @article{yoshizumi_coffer_collins_gaines_gao_jones_mcgregor_mcquillan_perin_tomkins_et al._2020, title={A Review of Geospatial Content in IEEE Visualization Publications}, DOI={10.1109/VIS47514.2020.00017}, abstractNote={Geospatial analysis is crucial for addressing many of the world’s most pressing challenges. Given this, there is immense value in improving and expanding the visualization techniques used to communicate geospatial data. In this work, we explore this important intersection – between geospatial analytics and visualization – by examining a set of recent IEEE VIS Conference papers (a selection from 2017-2019) to assess the inclusion of geospatial data and geospatial analyses within these papers. After removing the papers with no geospatial data, we organize the remaining literature into geospatial data domain categories and provide insight into how these categories relate to VIS Conference paper types. We also contextualize our results by investigating the use of geospatial terms in IEEE Visualization publications over the last 30 years. Our work provides an understanding of the quantity and role of geospatial subject matter in recent IEEE VIS publications and supplies a foundation for future meta-analytical work around geospatial analytics and geovisualization that may shed light on opportunities for innovation.}, journal={2020 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS 2020)}, author={Yoshizumi, Alexander and Coffer, Megan M. and Collins, Elyssa L. and Gaines, Mollie D. and Gao, Xiaojie and Jones, Kate and McGregor, Ian R. and McQuillan, Katie A. and Perin, Vinicius and Tomkins, Laura M. and et al.}, year={2020}, pages={51–55} }