@article{koch_dorning_van berkel_beck_sanchez_shashidharan_smart_zhang_smith_meentemeyer_et al._2019, title={Modeling landowner interactions and development patterns at the urban fringe}, volume={182}, ISSN={["1872-6062"]}, url={http://dx.doi.org/10.1016/j.landurbplan.2018.09.023}, DOI={10.1016/j.landurbplan.2018.09.023}, abstractNote={Population growth and unrestricted development policies are driving low-density urbanization and fragmentation of peri-urban landscapes across North America. While private individuals own most undeveloped land, little is known about how their decision-making processes shape landscape-scale patterns of urbanization over time. We introduce a hybrid agent-based modeling (ABM) – cellular automata (CA) modeling approach, developed for analyzing dynamic feedbacks between landowners’ decisions to sell their land for development, and resulting patterns of landscape fragmentation. Our modeling approach builds on existing conceptual frameworks in land systems modeling by integrating an ABM into an established grid-based land-change model – FUTURES. The decision-making process within the ABM involves landowner agents whose decision to sell their land to developers is a function of heterogeneous preferences and peer-influences (i.e., spatial neighborhood relationships). Simulating landowners’ decision to sell allows an operational link between the ABM and the CA module. To test our hybrid ABM-CA approach, we used empirical data for a rapidly growing region in North Carolina for parameterization. We conducted a sensitivity analysis focusing on the two most relevant parameters—spatial actor distribution and peer-influence intensity—and evaluated the dynamic behavior of the model simulations. The simulation results indicate different peer-influence intensities lead to variable landscape fragmentation patterns, suggesting patterns of spatial interaction among landowners indirectly affect landscape-scale patterns of urbanization and the fragmentation of undeveloped forest and farmland.}, journal={LANDSCAPE AND URBAN PLANNING}, author={Koch, Jennifer and Dorning, Monica A. and Van Berkel, Derek B. and Beck, Scott M. and Sanchez, Georgina M. and Shashidharan, Ashwin and Smart, Lindsey S. and Zhang, Qiang and Smith, Jordan W. and Meentemeyer, Ross K. and et al.}, year={2019}, month={Feb}, pages={101–113} }
 @article{shashidharan_vatsavai_meentemeyer_2018, title={FUTURES-DPE: Towards Dynamic Provisioning and Execution of Geosimulations in HPC environments}, DOI={10.1145/3274895.3274948}, abstractNote={Geosimulations using computer simulation models provideGI scientists an effective way to study complex geographic phenomena and predict future outcomes. Typically, geosimulations are developed to execute in an HPC environment with parallel and distributed execution capabilities. However, traditional HPC environments limit these simulations to a static runtime environment, where resources for execution must be decided before execution. Traditional simulation approaches such as a data parallel approach assigns fixed computing resources on every unit of data (e.g., a tile or a county). However, in many practical situations, a user may want to assign additional computing resources to speedup or perform more computation in a specific region. For example, in an urban growth model (UGM) simulation, to explore the outcomes of changes due to urban policy in a tile or a group of tiles at a given time-step, an urban geographer may want to assign more computing resources to those group of tiles to quickly determine impacts of policy on urbanization. In the absence of a dynamic resource allocation mechanism, the utility of a geosimulation to explore what-if scenarios on-the-fly is limited to pre-allocated computing resources. Thus, to effectively leverage existing resources, we first design a co-scheduling approach for geosimulations in a resource constrained HPC environment. We then present a second design for a geosimulation which allows dynamic provisioning of resources in an HPC environment based on run-time users' demands. Finally, to demonstrate the utility of the two approaches we modify the FUTURES geosimulation to support computationally expensive high-resolution simulation in regions of interest (ROIs) as specified by a user using the FUTURES-DPE framework.}, journal={26TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2018)}, author={Shashidharan, Ashwin and Vatsavai, Ranga Raju and Meentemeyer, Ross K.}, year={2018}, pages={464–467} }
 @article{shashidharan_berkel_vatsavai_meentemeyer_2016, title={pFUTURES: A Parallel Framework for Cellular Automaton Based Urban Growth Models}, volume={9927}, ISBN={["978-3-319-45737-6"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-45738-3_11}, abstractNote={Simulating structural changes in landscape is a routine task in computational geography. Owing to advances in sensing and data collection technologies, geospatial data is becoming available at finer spatial and temporal resolutions. However, in practice, these large datasets impede land simulation based studies over large geographic regions due to computational and I/O challenges. The memory overhead of sequential implementations and long execution times further limit the possibilities of simulating future urban scenarios. In this paper, we present a generic framework for co-ordinating I/O and computation for geospatial simulations in a distributed computing environment. We present three parallel approaches and demonstrate the performance and scalability benefits of our parallel implementation pFUTURES, an extension of the FUTURES open-source multi-level urban growth model. Our analysis shows that although a time synchronous parallel approach obtains the same results as a sequential model, an asynchronous parallel approach provides better scaling due to reduced disk I/O and communication overheads.}, journal={GEOGRAPHIC INFORMATION SCIENCE, (GISCIENCE 2016)}, author={Shashidharan, Ashwin and Berkel, Derek B. and Vatsavai, Ranga Raju and Meentemeyer, Ross K.}, year={2016}, pages={163–177} }