In this seminar, you will learn how to use Web AppBuilder to create powerful GIS apps that run on any device without writing a single line of code. You will also learn how to quickly build web apps with your data, selection of widgets, and the theme you choose, to make them available to your organization.This seminar was developed to support the following:ArcGIS OnlineWeb AppBuilder for ArcGISWeb AppBuilder for ArcGIS (Developer Edition) 1.0

UNIDO pub. Training handbook providing introduction to geographic information systems (GIS) for industrial development of developing countries - covers (1) general objectives and objectives for training, decision making for sustainable development (2) the GIS data model and raster-based GIS applications (3) topological overlay and mathematical operations with GIS (4) IDRISI programme modules (5) ArcView programme modules (6) getting IDRISI and ArcView started (7) case studys. Bibliography. Statistics, diagrams. Additional references: computer, computer programmes, electronic data processing, mathematical analysis.

800+ GIS Engineers with 25+ years of experience in geospatial, We provide following as Advance Geospatial Services:

Analytics (AI) Change detection Feature extraction Road assets inventory Utility assets inventory Map data production Geodatabase generation Map data Processing /Classifications
Contour Map Generation Analytics (AI) Change Detection Feature Extraction Imagery Data Processing Ortho mosaic Ortho rectification Digital Ortho Mapping Ortho photo Generation Analytics (Geo AI) Change Detection Map Production Web application development Software testing Data migration Platform development

AI-Assisted Data Mapping Pipeline AI models trained on millions of images are used to predict traffic signs, road markings , lanes for better and faster data processing

Our Value Differentiator

Experience & Expertise -More than Two decade in Map making business with 800+ GIS expertise -Building world class products with our expertise service division & skilled project management -International Brand “Mappls” in California USA, focused on “Advance -Geospatial Services & Autonomous drive Solutions”

Value Added Services -Production environment with continuous improvement culture -Key metrics driven production processes to align customer’s goals and deliverables -Transparency & visibility to all stakeholder -Technology adaptation by culture

Flexibility -Customer driven resource management processes -Flexible resource management processes to ramp-up & ramp-down within short span of time -Robust training processes to address scope and specification changes -Priority driven project execution and management -Flexible IT environment inline with critical requirements of projects

Quality First -Delivering high quality & cost effective services -Business continuity process in place to address situation like Covid-19/ natural disasters -Secure & certified infrastructure with highly skilled resources and management -Dedicated SME team to ensure project quality, specification & deliverables

Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

Purpose: The mission of the Geometric Design Laboratory (GDL) is to support the Office of Safety Research and Development in research related to the geometric design of roadways and the impacts on safety. The GDL provides technical support to develop, maintain, and enhance tools for the safety evaluation of highway geometric design alternatives. This includes coordination of the Highway Safety Manual (HSM) with related tools, e.g., the Interactive Highway Safety Design Model (IHSDM) and SafetyAnalyst. The GDL supports the HSM through implementation of HSM methods in IHSDM software; by providing technical support to HSM users; by performing HSM-related technology facilitation; and by conducting HSM-related training and research.The GDL also contributes to Federal Highway Administration's (FHWA's) Roadway Safety Data Program (RSDP) initiatives to advance State and local safety data systems and safety data analyses by supporting the use of Geographic Information Systems (GIS) for advancing the quantification of highway safety (e.g., through the integration of GIS with highway safety analysis tools); and supports the Safety Training and Analysis Center (STAC) in its mission to assist the research community and State departments of transportation (DOTs) in using data from the second Strategic Highway Research Program's (SHRP2) Naturalistic Driving Study (NDS) and Roadway Information Database (RID).Laboratory Description: GDL staff focuses on the following tasks.Research: Support IHSDM, Highway Safety Manual, and other highway safety-related research efforts.Software Development: Support the full life cycle of IHSDM software development, including developing functional specifications; performing verification and validation of the models that are core IHSDM components; providing recommendations to the IHSDM software developer on all facets of the software (e.g., the graphical user interface, output/reporting); preparing IHSDM documentation; performing alpha testing of IHSDM software; and coordinating the beta testing of IHSDM software by end users. The GDL also helps coordinate the interaction of key players in IHSDM software development, including research contractors, software developers, end users, and commercial computer-aided design (CAD)/roadway design software vendors.Technology Facilitation: Support technology facilitation for the IHSDM and HSM. The GDL provides the sole source of technical support to IHSDM users and provides technical support to HSM users. GDL markets IHSDM and HSM to decisionmakers and potential end users, and participates in developing and delivering IHSDM/HSM training.Laboratory Capabilities: The staff of the GDL includes professionals with expertise in transportation engineering and familiarity with software development, which allows the GDL to support IHSDM development in various ways and to assume a unique coordination role. The GDL's transportation engineering expertise supports the laboratory's function of reviewing and assisting the development of the engineering models included in IHSDM for evaluating the safety of roadway designs. By combining transportation engineering and software development expertise, the GDL has the unique ability to evaluate software from both the software developer and end-user perspective.Communications and engineering skills help GDL staff to understand the needs of the audience (e.g., design engineers), thereby supporting effective technical assistance to end users.IHSDM development is a long-term effort, involving many research contractors, software developers, and FHWA staff. In addition, FHWA seeks input from end users and user organizations to help ensure that IHSDM is responsive to user needs. The staff of the GDL helps coordinate the interaction of all those involved with IHSDM development.Staff at the GDL participates in HSM development and technology facilitation. In addition, the IHSDM Crash Prediction Module is a faithful implementation of HSM Part C (Predictive Method). Therefore, GDL staff is well equipped to support HSM-related activities.Laboratory Equipment: The GDL is equipped with computer hardware and software typically employed by users of IHSDM, including commercial CAD/roadway design software.Laboratory Services: The GDL supports the HSM through implementation of HSM methods in IHSDM software; by providing technical support to HSM users; by performing HSM-related technology facilitation; and by conducting HSM-related research.To develop and promote IHSDM, GDL staff provides or has provided the following services:For all IHSDM safety evaluation modules (Crash Prediction, Design Consistency, Intersection Review, Policy Review, Traffic Analysis and Driver/Vehicle), the GDL conducts software testing to verify, validate, and evaluate the IHSDM software system and develops and/or finalizes the software's functional specifications.Participates in development and delivery of IHSDM training.Provides the sole source of technical assistance to IHSDM users ( ihsdm.support@dot.gov; 202-493-3407).Supports coordination and integration of IHSDM with civil design software packages.Develops, reviews, maintains, and enhances documentation for IHSDM users.Conducts technical reviews and prepares review comments on contract research deliverables.Provides technical support in the development, production, and dissemination of IHSDM-related marketing materials.Provides technical content for the IHSDM Web site.The GDL also contributes to FHWA Roadway Safety Data Program (RSDP) initiatives to advance State and local safety data systems and safety data analyses by supporting the use of GIS for advancing the quantification of highway safety; e.g., through the integration of GIS with highway safety analysis tools (including extraction of data from GIS for input to safety analyses and representation of safety analysis results in the GIS environment). Such contributions support efforts by State and local agencies to:Extract roadway geometrics from GIS/GPS data.Develop GIS-based tools for collecting roadway inventory data.Process data gathered using instrumented vehicles (e.g., LiDAR).Leverage GIS/GPS data for populating safety databases and performing safety analyses (e.g., safety management - HSM Part B, and crash prediction - HSM Part C). The GDL supports the Safety Training and Analysis Center (STAC) in assisting the research community and State DOTs in using data from the SHRP2 Naturalistic Driving Study (NDS) and Roadway Information Database (RID); e.g., by assessing analytical possibilities associated with GIS data linkages to the RID.

description: Quivira National Wildlife Refuge was established in 1955, and a detailed vegetation map was not available for management purposes. With the present development of a biological program and Comprehensive Conservation Plan (CCP), a baseline vegetation map of the refuge was identified as a necessity. Development of the vegetation map and associated report was a multi-step process. Aerial photography (NAIP, 2008) was used with eCognition to create polygons of different plant communities based on the likeness of surrounding pixels in the area. Prior to ground-truthing, the following activities were accomplished: training on vegetation mapping using GIS (previous experience and National Conservation Training Center course), creation of an vegetation association and alliance dichotomous key, development of a refuge plant key and identification skills, and preparation of maps for ground truthing. Once out in the field dominant plants were identified for appropriate vegetation alliance and association classification, plant specimens were collected for the refuge herbarium as necessary and additional observations and photos were gathered for the report. Over the course of the project, classification data was entered into a GIS and polygons were appropriately modified to create the final map. At Quivira, results found a total of 42 alliances and 43 associations.The most dominant plants throughout the refuge in 2008 based on canopy cover were saltgrass, plum, little bluestem and cottonwood. The number of alliances and associations found on the refuge show high species diversity.; abstract: Quivira National Wildlife Refuge was established in 1955, and a detailed vegetation map was not available for management purposes. With the present development of a biological program and Comprehensive Conservation Plan (CCP), a baseline vegetation map of the refuge was identified as a necessity. Development of the vegetation map and associated report was a multi-step process. Aerial photography (NAIP, 2008) was used with eCognition to create polygons of different plant communities based on the likeness of surrounding pixels in the area. Prior to ground-truthing, the following activities were accomplished: training on vegetation mapping using GIS (previous experience and National Conservation Training Center course), creation of an vegetation association and alliance dichotomous key, development of a refuge plant key and identification skills, and preparation of maps for ground truthing. Once out in the field dominant plants were identified for appropriate vegetation alliance and association classification, plant specimens were collected for the refuge herbarium as necessary and additional observations and photos were gathered for the report. Over the course of the project, classification data was entered into a GIS and polygons were appropriately modified to create the final map. At Quivira, results found a total of 42 alliances and 43 associations.The most dominant plants throughout the refuge in 2008 based on canopy cover were saltgrass, plum, little bluestem and cottonwood. The number of alliances and associations found on the refuge show high species diversity.

Professional Development Section training bulletin manual focuses on community relations, legal issues, patrol procedures and officer safety.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

This web map presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. Esri created this vector tile basemap from the Daylight map distribution of OSM data, which is supported by Facebook and supplemented with additional data from Microsoft. This version of the map is rendered using OSM cartography. The OSM Daylight map will be updated every month with the latest version of OSM Daylight data.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site:www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this enhanced vector basemap available to the ArcGIS user and developer communities.

The rapid expansion of geospatial technology has brought the need for new skills to be able to understand and apply the newly available tools – not just for GIS professionals but also for engineers, urban planners, rural development specialists, conservationists and many others. Experience from other countries has shown a rapid growth in the use of of geospatial technologies in government ministries, in the private and development sectors, and in the academic sector as a source of learning and research. The Myanmar Information Management Unit / MIMU has organised this two-day Symposium on May 24-25 with the support of the Government of Canada. It will bring together over 120 participants to discuss the evolving use of geospatial technologies in Myanmar and how training can be best oriented to meet these needs. Participants include academics from the 25 universities across the country offering courses in the use of geospatial technologies, representatives of government departments, the private and development sectors. International experts from leading institutions from the Netherlands and Thailand will also join to share experience and approaches. Key issues which will be explored in the Symposium: Can skilled geospatial workers in Myanmar meet the needs of today’s academic, government, private and development sector? What steps should training institutions and professionals take to ensure capacity for future needs, as the geospatial field continues its evolution.

To create this app:

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

UNIDO pub. Final report on the Workshop "Industrial Development in Coastal Areas of South East Asia" - covers (1) background (2) participants presentations on: remote sensing, mathematical modeling (ref: mathematical analysis), GIS, pollution sources and industryal development in coastal areas, support systems for coastal management (3) workshop programme (4) project's proposals: linkage of GIS and modeling, coastal ecologycal system management in urban areas and industrial areas, methodologys for infrastructure planning in the environment, development of training programmes for coastal zone management in southern Vietnam. List of participants.

Professional Development Section training bulletin manual focuses on community relations, legal issues, patrol procedures and officer safety.

Manually digitizing the track of an object can be a slow process. This model automates the object tracking process significantly, and hence speeds up motion imagery analysis workflows. It can be used with the Motion Imagery Toolset found in the Image Analyst extension to track objects. The detailed workflow and description of the object tracking capability in ArcGIS Pro can be found here.This model can be used for applications such as object follower and surveillance of stationary objects. It does not perform very well in case there are sudden camera shakes or abrupt scale changes.Using the modelFollow the guide to use the model. The model can be used with the Motion Imagery tools in ArcGIS Pro 2.8 and onwards. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.InputObject to track marked as a bounding box in 8-bit, 3-band high resolution full motion video / motion imagery. Recommended object size is greater than 15x15 (in pixels).OutputBounding box depicting object location in successive frames.Applicable geographiesThis model is expected to work well in all regions globally for any generic-type of objects of interest. However, results can vary for motion imagery that are statistically dissimilar to the training data.Model architectureThis model uses the SiamMask model architecture implemented in ArcGIS API for Python.Accuracy metricsThe model has an average precision score of 0.853. Training dataThe model was trained using image sequences from the DAVIS dataset licensed under CC BY 4.0 license, and further fine-tuned on aerial motion imagery.Sample resultsHere are a few results from the model.

Map of each SKYWARN Storm Identification and Safety training scheduled across Kansas for year 2024. Information such as the date, day of week, time, county, and location are visible for each point on the map.

The Commonwealth of Kentucky through the Commonwealth Office of Technology's Division of Geographic Information (DGI) in conjunction with the Kentucky GIS Community has made available a wealth of GIS-related information, data sets and maps. These resources support education and training, research, and policy development for a multitude of organizations in Kentucky and across the United States.The Commonwealth Map is a statewide digital basemap available via the Internet for interactive mapping, geographic data querying, and downloading. As a collaborative effort of local, state, and federal partners, this initiative is designed to facilitate public, non-profit, and private sector GIS development, utilization, innovation, and data sharing.This web map also includes a great set of bookmarks prepared by the Kentucky Geography Network.Kentucky Division of Geographic Information: https://gis.ky.gov/Kentucky Geography Network: https://kygeonet.ky.govYou can access the Kentucky Commonwealth Map viewer here: https://kygeonet.ky.gov/tcm/ArcMap users can also access a ready to use map document (MXD file) for Kentucky that references this service. Click to launch. Requires ArcGIS 9.3 or more recent: MXD. This map document also includes the bookmarks prepared by the Kentucky Geography Network.More details about the Commonwealth Map of Kentucky map service used in this web map can be found here.

Probability of Development, Northeast U.S. is one of a suite of products from the Nature’s Network project. Nature’s Network is a collaborative effort to identify shared priorities for conservation in the Northeast, considering the value of fish and wildlife species and the natural areas they inhabit.This index represents the integrated probability of development occurring sometime between 2010 and 2080 at the 30 m cell level. It was based on models of historical patterns of urban growth in the Northeast, including the type (low intensity, medium intensity and high intensity), amount and spatial pattern of development, and incorporates the influence of factors such as geophysical conditions (e.g., slope, proximity to open water), existing secured lands, and proximity to roads and urban centers. The projected amount of new development is downscaled from county level forecasts based on a U.S. Forest Service 2010 Resources Planning Act (RPA) assessment. A complementary product, Probability of Development, 2030, Northeast U.S., estimates the probability of development over a shorter time-scale.Note: based on revisions of the sprawl model, this version was revised in July 2017 to better reflect relatively higher probabilities of development in close vicinity to roads, which is most evident in rural areas.Description and DerivationThe derivation of the integrated probability of development layer was complex. Please consult the detailed technical documentation for a full description of the background data used, the computation of integrated probabilities from a stochastic model, and information about the related urban growth model. The following is a summary of the five major steps of the derivation: 1) Determining historical patterns of growthTo understand how past patterns of development have occurred, historical data from NOAA (for Maine and Massachusetts) and the Chesapeake Bay Watershed Landcover Data Series were obtained for the years 1984 (Chesapeake Bay only), 1996, and 2006. The data were used to model the occurrence of six different development transition types: New growthundeveloped to low-intensity (20-49% impervious surface; e.g., single-family homes)undeveloped to medium-intensity (50-79% impervious surface; e.g., small-lot single-family homes)undeveloped to high-intensity (80-100% impervious surface; e.g., apartment complexes and commercial/industrial development) Intensificationlow- to medium-intensitylow- to high-intensitymedium- to high-intensity Separate models were developed to represent development patterns at model points representing landscapes differing along two dimensions: intensity of development and amount of open water. Predictor variables in the models account for the intensity of existing development and landscape context (e.g. intensity and distance of nearest roads, amount of open water). Analysis of the historical data was based on dividing the landscape into “training windows,” 15km on a side, to determine the historical distribution of transition types and the total amount of historical development. 2) Application to current landscapesFuture patterns of development were projected based on the observed historical patterns. As the first step in this process, the entire Northeast was subdivided into 5km “application panes,” each of which was the center pane of a (3 x 3) “application window”, 15 km on a side. Each of these overlapping application windows was then matched to the three most similar training windows on the basis of intensity of development from the UMass integrated landcover layer, (derived in turn from the 2011 National Landcover Database and other sources), as well as geographic proximity, amount of open water, and density of roads. . For each application window, according to how it mapped on to the dimensions of development and open water modelled above, the relative probability of each of the six development transition types was determined on a scale of 30m cells. 3) Predictions for changing land-useFuture urban acreage by county was predicted as part of an assessment for the U.S. Forest Service 2010 Resources Planning Act. The derivation of this product, the new growth forecasted for the 70 years between 2010 and 2080 was transformed into demand in units of 30m cells. Demand for each county (or census Core Based statistical Area, where relevant) was allocated to the corresponding application windows based on the average of the total amount of historical development in the three matched training windows. 4) Combining models of past and predictions for the futureThe relative probability of a transition type occurring in each cell in a window was used to distribute the allocated demand of new growth throughout the window. The result was an actual probability of development for the transition occurring sometime between 2010- 2080 at the 30 m cell level. Already existing urban land-use was intensified (i.e., transitions 4-6) in proportion to historic patterns determined from the matched training windows, and distributed according to the probability of those transition types across the cells in the window. The combining of probabilities and demand to distribute development to cells was done for each transition type in turn; thus, each cell received a separate probability of being developed through each of the six transition types. Through the application of this process in every application window, an actual probability of development was determined for each cell with reference to nine slightly different contexts corresponding to each of the overlapping windows in which the pane was situated. 5) Smoothing and integrationAn additional step was used to create a smooth and continuous probability of development surface, not subject to abrupt differences along arbitrary boundaries. Cell by cell, actual probabilities of development from each of the overlapping windows were combined such that the closer to a window’s center a cell was located, the more weight the probability derived from it was given. Consequently, each cell had one weighted average probability that was part of a continuous probability of development surface for each transition type. Finally, the probability of development by each of six transition types was integrated for each cell. More weight was given to new growth, such that the probability of undeveloped land becoming urban had more impact than the probability of an intensification of development. The final product is a single layer of the integrated probability of development by 2080, extending across the entire Northeast on the scale of 30 m cells.Known Issues and Uncertainties As with any project carried out across such a large area, the Probability of Development dataset is subject to limitations. The results by themselves are not a prescription for on-the-ground action; users are encouraged to verify, with field visits and site-specific knowledge, the value of any areas identified in the project. Known issues and uncertainties include the following:Although this index is a true probability, it is best used in a relative manner to compare values from one location to anotherThe GIS data upon which this product was based, especially the National Land Cover Dataset (NLCD), are imperfect. Errors of both omission and commission affect the mapping of current development and in turn, models of the probability of future development. Likewise, the forecasts in the 2010 Resources Planning Act assessment, the basis of the projected demand for new growth, contains uncertainties. While the model is anticipated to generally correctly indicate where development is likely to occur, predictions at the cell level are not expected to be highly reliable.Users are cautioned against using the data on too small an area (for example, a small parcel of land), as the data may not be sufficiently accurate at that level of resolution.This model is built on the assumption that future patterns of development will match patterns in the past.It is important to recognize that the integrated probability of development is highest near existing roads, largely because the urban growth model does not attempt to predict the building of new roads and the development associated with them, nor does it incorporate county or town level planning for infrastructure. Because proximity to roads is an important and dominant predictor of development at the 30- m cell level in the model, the integrated probability of development surface is heavily weighted towards existing roads. It is not specifically designed to predict where a subdivision might be developed in the future.

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map assesses and identifies communities that are Housing Disadvantaged according to Justice40 Initiative criteria. "Communities are identified as disadvantaged if they are in census tracts that:Experienced historic underinvestment OR are at or above the 90th percentile for the housing cost OR lack of green space OR lack of indoor plumbing OR lead paintAND are at or above the 65th percentile for low income"Census tracts in the U.S. and its territories that meet the criteria are shaded in blue colors. Suitable for dashboards, apps, stories, and grant applications.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 1.0 of the source data downloaded November 22, 2022.Use this map to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications.From the source:This data "highlights disadvantaged census tracts across all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged:If they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, orIf they are on land within the boundaries of Federally Recognized TribesCategories of BurdensThe tool uses datasets as indicators of burdens. The burdens are organized into categories. A community is highlighted as disadvantaged on the CEJST map if it is in a census tract that is (1) at or above the threshold for one or more environmental, climate, or other burdens, and (2) at or above the threshold for an associated socioeconomic burden.In addition, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income is also considered disadvantaged.Census tracts are small units of geography. Census tract boundaries for statistical areas are determined by the U.S. Census Bureau once every ten years. The tool utilizes the census tract boundaries from 2010. This was chosen because many of the data sources in the tool currently use the 2010 census boundaries."PurposeThe goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening tool"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.Note: A new version of this map was released November 22, 2022 and is available here. There are significant changes, see the Justice40 Initiative criteria for details.This map assesses and identifies communities that are disadvantaged according to Justice40 Initiative criteria. Census tracts in the U.S. and its territories that meet the criteria are shaded in dark blue. Suitable for dashboards, apps, stories, and grant applications.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 0.1 of the source data downloaded May 30, 2022.Use the accompanying layer to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications. See this blog post for more information.From the source:"Census tract geographical boundaries are determined by the U.S. Census Bureau once every ten years. This tool utilizes the census tract boundaries from 2010 because they match the datasets used in the tool. The U.S. Census Bureau will update these tract boundaries in 2020.Under the current formula, a census tract will be identified as disadvantaged in one or more categories of criteria:IF the tract is above the threshold for one or more environmental or climate indicators AND the tract is above the threshold for the socioeconomic indicatorsCommunities are identified as disadvantaged by the current version of the tool for the purposes of the Justice40 Initiative if they are located in census tracts that are at or above the combined thresholds in one or more of eight categories of criteria.The goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening toolPurpose"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40Browse the DataView the Data tab in the top right of the layer's item page to browse the data in a table and view the metadata available for each field, including field name, field alias, and a field description explaining what the field represents.