For the latter part of the 20th century, Northern Ireland, officially part of the United Kingdom but sharing an island with the Republic of Ireland, saw violence between the nationalists (mostly Roman Catholic background) and unionists (mostly Protestant background). The Good Friday Agreement of 1998 sought to end this conflict, by establishing peace between these two communities and guiding how Northern Ireland should be governed. But even 20 years on, Northern Ireland remains divided. Yet, hope is on the horizon. Young students in Lurgan—a town of 25,000 south of Belfast—are using Survey123 for ArcGIS to record data across sectarian lines. After analyzing the data collected, the students from conflicting backgrounds find that they aren't that different after all. This is how change begins. Join the students of Lurgan to learn how youth are using GIS to make a difference in their community.

Learn how defense and intelligence users can leverage ArcGIS GeoEvent Server and ArcGIS GeoAnalytics Server to connect to real-time data feeds and run analytics on the stored data. From tracking units in the field to analyzing intelligence feeds and weather, ArcGIS GeoEvent Server enables users to stay current on what is happening. When you want to analyze massive amounts of stored track and report data, ArcGIS GeoAnalytics Server uses distributed computing to return spatiotemporal insight helping you make better planning decisions.

2019 L-A Region Farmland Access & Food Economy Conference. Themes from small-group discussions, learning conversations, and follow-up survey. Prepared by: Francis Eanes and students in his Food Systems class. The first L-A Region Farmland Access and Food Economy conference was held March 7th, 2019 in the Auburn Senior Center. Following introductory remarks and facilitated storytelling, attendees broke into small groups to discuss challenges and opportunities as they pertain to the conference theme, followed by a series of focused “learning conversations” on more specific topics. This report summarizes themes from these small-group discussions and learning conversations (based on transcriptions of notes taken at each table), and a post-event evaluation survey filled out by 38 attendees

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It supplies LCMS Change classes for each year that are a refinement of the modeled LCMS Change classes (Slow Loss, Fast Loss, and Gain) and provide information on the cause of landscape change. See additional information about Change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. http://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Helmer, E. H., Ramos, O., del MLopez, T., Quinonez, M., and Diaz, W. (2002). Mapping the forest type and Land Cover of Puerto Rico, a component of the Caribbean biodiversity hotspot. Caribbean Journal of Science, (Vol. 38, Issue 3/4, pp. 165-183)Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pasquarella, V. J., Brown, C. F., Czerwinski, W., and Rucklidge, W. J. (2023). Comprehensive Quality Assessment of Optical Satellite Imagery Using Weakly Supervised Video Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2124-2134)Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual Land Cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261Pesaresi, M. and Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAStehman, S.V. (2014). Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. In International Journal of Remote Sensing (Vol. 35, pp. 4923-4939). https://doi.org/10.1080/01431161.2014.930207USDA National Agricultural Statistics Service Cropland Data Layer (2023). Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape/ (accessed 2024). USDA-NASS, Washington, DC.U.S. Geological Survey (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mU.S. Geological Survey (2023). Landsat Collection 2 Known Issues, accessed March 2023 at https://www.usgs.gov/landsat-missions/landsat-collection-2-known-issuesWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAYang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M., and Xian, G. (2018). A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies (https://www.sciencedirect.com/science/article/abs/pii/S092427161830251X), (pp. 108-123)Zhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of Land Cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011 This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The only book that teaches human geography themes within an ArcGIS Online environmentApplying new skills to established topics, this is how you want to examine human geography.Introduction to Human Geography Using ArcGIS Online combines a comprehensive examination of human geography with engaging activitiesusing the ArcGIS Online service.Birth rates are higher in one country than another. Why? Are there patterns? Are there correlations? Introduction to Human Geography Using ArcGIS Online readers are doers, using sophisticated software to actively explore, analyze, and answer these questions and many more. ArcGIS Online exercises in each chapter dig into those numbers and their spatial relationships, enhancing students’ grasp of geographic concepts.Instructors tailor classroom examples and homework assignments to local geography. Introduction to Human Geography Using ArcGIS Online bridges classroom lecture and live, current, interactive data for reinforced learning and a hybridized teaching approach.Human geography, meet The Science of Where™.AUDIENCEProfessional and scholarlyAUTHOR BIOJ. Chris Carter is a Professor of Geography at Long Beach City College. He has over 15 years of experience teaching human geography and GIS, as well as courses on world regional geography and economic geography. He has a BA in Sociology from the University of California, Berkeley, an MA from San Diego State University, an MBA from California State University, Long Beach, and a PhD in Geography from San Diego State University/University of California, Santa Barbara. He presented on crime patterns in Long Beach at the 2015 Esri Users’ Conference.Pub Date: Print: Feb 25, 2019 Digital: Feb 6, 2019ISBN: 9781589485181 9781589485198 Trim: 8.0 X 10.0Price: $74.99 USD $74.99 USD Pages: 440TABLE OF CONTENTSPrefaceAcknowledgmentsChapter 1: IntroductionChapter 2: PopulationChapter 3: MigrationChapter 4: Race and EthnicityChapter 5: Urban GeographyChapter 6: Food and AgricultureChapter 7: ManufacturingChapter 8: ServicesChapter 9: DevelopmentChapter 10: Cultural Geography—Folk and Popular Culture, Language,ReligionChapter 11: Political GeographyChapter 12: Humans and the Environment—Pollution and Climate ChangeAbout the AuthorIndexChris Carter | Interview with Esri Press | 2020/07/10 | 19m05s | Link.

Autorisation donnée au président de signer une convention stratégique avec l'établissement public foncier d'Ile-de-France (EPFIF)