Data for: A novel algorithm for calculating transition potential in cellular automata models of land-use/cover change
Contributors: Majid Shadman, Jagannath Aryal, Brett Bryan
... Land-use change dataset of Ahwaz, Iran.
Contributors: Francesco Macchione, Rosa De Santis, Pierfranco Costabile, Carmelina Costanzo
... The .txt file is the Python script able to import .2dm triangular grid as a Blender mesh. In order to run the file in Blender, one can use the following steps: 1. open a Text Editor view in Blender 2. go to Text >Open Text Block and open the .txt file 3. press run script Other comments are reported in the file.
Data for: A scalable modeling framework for massive machine learning-based land change simulations: Applying the k-means clustering scheme and the Spark cluster computing environment for model calibration
Contributors: Omrani Hichem, Marco Helbich, Bryan Pijanowski, Benoit Parmentier
... Three land use datasets from the USA (Wisconsin, Boston, and Boston). For the datasets we simulated the difference between urban-gain and non-urban persistence between two time periods. We excluded the urban class in the initial time because it is impossible for this urban class to have any urban-gain or non-urban persistence across two time points. Furthermore, a set of variables was deﬁned for each cell serving as driving factors. There are six variables in 1978 for Muskegon, eight variables in 1998 for Boston, and sixteen variables in 1990 for Wisconsin, as inputs and urban change maps between two time periods (1978-1998 in Muskegon, 1971-1999 in Boston, 1990-2000 in Wisconsin) as outputs. The cells of land use have a spatial resolution of 100, 2, and 30 meters in Muskegon, Boston, and Wisconsin. These datasets could be used for instance to perform a cross-model comparison among many other purposes.
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Contributors: Jonathan Koomey, Holmes Hummel, Zachary Schmidt, John Weyant
... These two files contain the data and analysis for the submitted article "Inside the Black Box", by Jonathan Koomey et al. The PFU file contains historical data used to create Figures 1 and 2 in the main text of the article, while the MESSAGE file contains the projections and data needed to create Figures 3 through 8 in the main text. There are many additional tabs in the workbooks that have historical value but are not directly relevant to the article itself. After the article is accepted we'll create tidier versions of these files that eliminate extraneous material, but we don't want to do that until we get final word from the editor and reviewers and make whatever additional changes they require in the analysis.
Data for: MANY-OBJECTIVE PORTFOLIO OPTIMIZATION APPROACH FOR STORMWATER MANAGEMENT PROJECT SELECTION ENCOURAGING DECISION MAKER BUY-IN
Contributors: Michael Di Matteo, Graeme Dandy, Holger Maier
... Additional Supporting Information (Files uploaded separately) • Data Set S1. An excel spreadsheet containing stormwater harvesting and green score determined by stakeholders, and calculations for costings and water quality performance of individual BMPs. Filename: ds01.xlsx • Data Set S2. A compressed file containing inputs and an executable for the Pareto Ant Colony Optimization Algorithm (PACOA) that can be run on a Windows desktop computer to replicate the optimization results. Filename: ds02.rar • Data Set S3. An excel spreadsheet containing the objective function values, decision options, and alternative data (catchment size, breakdown of benefits by Council) of the Pareto optimal solutions determined by sorting the optimization results for non-dominance in objective space. Filename: ds03.csv • Data Set S4. A .ddv that can be opened in the DiscoveryDV visual analytics program, containing interactive visualization of the Pareto optimal solution data from Data Set S3. Filename: ds04.ddv
Data for: Semantic Knowledge Network Inference Across a Range of Stakeholders and Communities of Practice
Contributors: Kostas Alexandridis, Barbara Lausche, Tetsu Sato, Jim Culter, Alex Webb, Shion Takemura
... tabular dataset containing the corpus narratives for each study, along with participant and field study attributes.
Contributors: David Machac, Dario Del Giudice, Jörg Rieckermann, Peter Reichert, Carlo Albert
... Software used to generate this dataset is to be found in the repository https://github.com/machacd/mechemu .
Contributors: Daniel B. Bernet, Mirjam Stawicki, Andreas Paul Zischg, Volker Prasuhn, Rolf Weingartner
... Data regarding observed surface water flood (SWF) events are sparse or difficult and tedious to obtain. This dataset documents eight different SWF events in Switzerland. It comprises all data that are usually required for modeling SWFs, except digital terrain model data, for which only links to corresponding data providers can be given. For each event, the dataset provides the study site perimeters, coarse soil data, event-specific land use data as well as the corresponding hyetographs inferred from a blended radar and rain gauge dataset. Most importantly, the dataset includes observed inundated areas that were mapped based on all available material, which documented the corresponding SWF event. The material included direct documentations of SWFs (photographs, videos), indirect indications based on the traces of SWFs (aerophotographs, photographs, fieldwork), and witness reports. Thus, the dataset is not only suitable for quickly setting up a SWF modeling approach, but also for calibrating, validating and testing modeling approaches based on observations. The dataset contains eight SWF events widely distributed in the northwestern part of Switzerland, which includes seven different study sites, i. e., two different events were observed at the same location. Five SWF events were triggered by relatively short and intense precipitation, whereas the remaining three SWF events were caused by relatively long and weak precipitation. Overall, the dataset covers a wide range of different geographical settings. Thus, it is possible to test modeling approaches in different environments and circumstances. The dataset is available in English and German. The only difference is that the German version includes an additional summary report for each event.
Data for: A polynomial approximation of the traffic contributions for kriging-based interpolation of urban air quality model
Contributors: Maxime Beauchamp, Laure Malherbe, Chantal de Fouquet, Laurent Létinois
... This program aims at interpolating the outputs of atmospheric urban dispersion models. The proposed new geostatistical method enables to distinguish information along and across the roads in the estimation. A set of polynomial drifts with unknown coefficients, inspired by the exponential function, is used as external drift in the kriging. All the additional informations to run the code can be directly found as comments in the program.
Data for: Development of an automated and open source GIS tool for reproducing the HAND terrain model
Contributors: Omid Rahmati, Antonio Donato Nobre, Assefa Melesse, Aiding Kornejady, Mahmood Samadi
... The HAND tool was developed using Python programming language which uses functionalities of a commercial geographic information system for constructing the HAND model and terrain map. This tool can be used in ArcGIS 10.2.