The data provides a summary of the state of development practice for Geographic Information Systems (GIS) software (as of August 2017). The summary is based on grading a set of 30 GIS products using a template of 56 questions based on 13 software qualities. The products range in scope and purpose from a complete desktop GIS systems, to stand-alone tools, to programming libraries/packages.

The template used to grade the software is found in the TabularSummaries.zip file. Each quality is measured with a series of questions. For unambiguity the responses are quantified wherever possible (e.g.~yes/no answers). The goal is for measures that are visible, measurable and feasible in a short time with limited domain knowledge. Unlike a comprehensive software review, this template does not grade on functionality and features. Therefore, it is possible that a relatively featureless product can outscore a feature-rich product.

A virtual machine is used to provide an optimal testing environments for each software product. During the process of grading the 30 software products, it is much easier to create a new virtual machine to test the software on, rather than using the host operating system and file system.

The raw data obtained by measuring each software product is in SoftwareGrading-GIS.xlsx. Each line in this file corresponds to between 2 and 4 hours of measurement time by a software engineer. The results are summarized for each quality in the TabularSummaries.zip file, as a tex file and compiled pdf file.

The primary intent of this workshop is to provide practical training in using Statistics Canada geography files with the leading industry standard software: Environmental Systems Research Institute, Inc.(ESRI) ArcGIS 9x. Participants will be introduced to the key features of ArcGIS 9x, as well as to geographic concepts and principles essential to understanding and working with geographic information systems (GIS) software. The workshop will review a range of geography and attribute files available from Statistics Canada, as well as some best practices for accessing this information. A brief overview of complementary data sets available from federal and provincial agencies will be provided. There will also be an opportunity to complete a practical exercise using ArcGIS9x. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-221.)

In this blog I’ll share the workflow and tools used in the GIS part of this analysis. To understand where crashes are occurring, first the dataset had to be mapped. The software of choice in this instance was ArcGIS, though most of the analysis could have been done using QGIS. Heat maps are all the rage, and if you want to make simple heat maps for free and you appreciate good documentation, I recommend the QGIS Heatmap plugin. There are also some great tools in the free open-source program GeoDa for spatial statistics.

The USDA Long-Term Agroecosystem Research was established to develop national strategies for sustainable intensification of agricultural production. As part of the Agricultural Research Service, the LTAR Network incorporates numerous geographies consisting of experimental areas and locations where data are being gathered. Starting in early 2019, two working groups of the LTAR Network (Remote Sensing and GIS, and Data Management) set a major goal to jointly develop a geodatabase of LTAR Standard GIS Data Layers. The purpose of the geodatabase was to enhance the Network's ability to utilize coordinated, harmonized datasets and reduce redundancy and potential errors associated with multiple copies of similar datasets. Project organizers met at least twice with each of the 18 LTAR sites from September 2019 through December 2020, compiling and editing a set of detailed geospatial data layers comprising a geodatabase, describing essential data collection areas within the LTAR Network. The LTAR Standard GIS Data Layers geodatabase consists of geospatial data that represent locations and areas associated with the LTAR Network as of late 2020, including LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This geodatabase was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. The creation of the geodatabase began with initial requests to LTAR site leads and data managers for geospatial data, followed by meetings with each LTAR site to review the initial draft. Edits were documented, and the final draft was again reviewed and certified by LTAR site leads or their delegates. Revisions to this geodatabase will occur biennially, with the next revision scheduled to be published in 2023. Resources in this dataset:Resource Title: LTAR Standard GIS Data Layers, 2020 version, File Geodatabase. File Name: LTAR_Standard_GIS_Layers_v2020.zipResource Description: This file geodatabase consists of authoritative GIS data layers of the Long-Term Agroecosystem Research Network. Data layers include: LTAR site locations, LTAR site points of contact and street addresses, LTAR experimental boundaries, LTAR site "legacy region" boundaries, LTAR eddy flux tower locations, and LTAR phenocam locations.Resource Software Recommended: ArcGIS,url: esri.com Resource Title: LTAR Standard GIS Data Layers, 2020 version, GeoJSON files. File Name: LTAR_Standard_GIS_Layers_v2020_GeoJSON_ADC.zipResource Description: The contents of the LTAR Standard GIS Data Layers includes geospatial data that represent locations and areas associated with the LTAR Network as of late 2020. This collection of geojson files includes spatial data describing LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This dataset was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. Resource Software Recommended: QGIS,url: https://qgis.org/en/site/

Update: We updated the data set in March 2022 by adding newly published papers and by providing more insights on how we analyzed them. Details can be found in the file " SEnti-SMS.xlsx".

Update: The updated version (-v2) contains the results of one more snowballing iteration and extracted information on the accuracy of the used methods.

In 2020, we conducted a systematic literature review to explore the development and application of sentiment analysis tools in software engineering.

Information on the execution of the SLR, its scope, the search string, etc. are presented in the paper linked below.

Weekly snapshot of Cleveland City Planning Commission datasets that are featured on the City Planning Zoning Viewer. For the official, most current record of zoning info, use the CPC Zoning Viewer.This file is an open-source geospatial (GIS) format called GeoPackage, which can contain multiple layers. It is similar to Esri's file geodatabase format. Free and open-source GIS software like QGIS, or software like ArcGIS, can read the information to view the tables and map the information.It includes the following mapping layers officially maintained by Cleveland City Planning Commission:Planner Assignment AreasPlanned Unit Development OverlayResidential FacilitiesResidential Facilities 1000 ft. BufferPolice DistrictsLandmarks / Historic LayersLocal Landmark PointsLocal Landmark ParcelsLocal Landmark DistrictsNational Historic DistrictsCentral Business DistrictDesign Review RegionsDesign Review DistrictsOverlay Frontage LinesForm & PRO Overlay DistrictsLive-Work Overlay DistrictsSpecific SetbacksStreet CenterlinesZoningUpdate FrequencyWeekly on Mondays at 4:30 AMContactCity Planning Commission, Zoning & Technology

This file presents the catalog of metrics and the topic independent classification done.

Links to recordings of the Integrated Services Program and 9-1-1 & Geospatial Services Bureau webinar series, including NG9-1-1 GIS topics such as: data preparation; data provisioning and maintenance; boundary best practices; and extract, transform, and load (ETL). Offerings include:Topic: Virginia Next Generation 9-1-1 Dashboard and Resources Update Description: Virginia recently updated the NG9-1-1 Dashboard with some new tabs and information sources and continues to develop new resources to assist the GIS data work. This webinar provides an overview of changes, a demonstration of new functionality, and a guide to finding and using new resources that will benefit Virginia public safety and GIS personnel with roles in their NG9-1-1 projects. Wednesday 16 June 2021. Recording available at: https://vimeo.com/566133775Topic: Emergency Service Boundary GIS Data Layers and Functions in your NG9-1-1 PSAP Description: Law, Fire, and Emergency Medical Service (EMS) Emergency Service Boundary (ESB) polygons are required elements of the NENA NG9-1-1 GIS data model stack that indicate which agency is responsible for primary response. While this requirement must be met in your Virginia NG9-1-1 deployment with AT&T and Intrado, there are quite a few ways you could choose to implement these polygons. PSAPs and their GIS support must work together to understand how this information will come into a NG9-1-1 i3 PSAP and how it will replace traditional ESN information in order to make good choices while implementing these layers. This webinar discusses:the function of ESNs in your legacy 9-1-1 environment, the role of ESBs in NG9-1-1, and how ESB information appears in your NG9-1-1 PSAP. Wednesday, 22 July 2020. Recording available at: https://vimeo.com/441073056#t=360sTopic: "The GIS Folks Handle That": What PSAP Professionals Need to Know about the GIS Project Phase of Next Generation 9-1-1 DeploymentDescription: Next Generation 9-1-1 (NG9-1-1) brings together the worlds of emergency communication and spatial data and mapping. While it may be tempting for PSAPs to outsource cares and concerns about road centerlines and GIS data provisioning to 'the GIS folks', GIS staff are crucial to the future of emergency call routing and location validation. Data required by NG9-1-1 usually builds on data that GIS staff already know and use for other purposes, so the transition requires them to learn more about PSAP operations and uses of core data. The goal of this webinar is to help the PSAP and GIS worlds come together by explaining the role of the GIS Project in the Virginia NG9-1-1 Deployment Steps, exploring how GIS professionals view NG9-1-1 deployment as a project, and fostering a mutual understanding of how GIS will drive NG9-1-1. 29 January 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225474Topic: Getting Your GIS Data from Here to There: Processes and Best Practices for Extract, Transform and Load (ETL) Description: During the fall of 2019, VITA-ISP staff delivered workshops on "Tools and Techniques for Managing the Growing Role of GIS in Enterprise Software." This session presents information from the workshops related to the process of extracting, transforming, and loading data (ETL), best practices for ETL, and methods for data schema comparison and field mapping as a webinar. These techniques and skills assist GIS staff with their growing role in Next Generation 9-1-1 but also apply to many other projects involving the integration and maintenance of GIS data. 19 February 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225007Topic: NG9-1-1 GIS Data Provisioning and MaintenanceDescription: VITA ISP pleased to announce an upcoming webinar about the NG9-1-1 GIS Data Provisioning and Maintenance document provided by Judy Doldorf, GISP with the Fairfax County Department of Information Technology and RAC member. This document was developed by members of the NG9-1-1 GIS workgroup within the VITA Regional Advisory Council (RAC) and is intended to provide guidance to local GIS and PSAP authorities on the GIS datasets and associated GIS to MSAG/ALI validation and synchronization required for NG9-1-1 services. The document also provides guidance on geospatial call routing readiness and the short- and long-term GIS data maintenance workflow procedures. In addition, some perspective and insight from the Fairfax County experience in GIS data preparation for the AT&T and West solution will be discussed in this webinar. 31 July 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224774Topic: NG9-1-1 Deployment DashboardDescription: I invite you to join us for a webinar that will provide an overview of our NG9-1-1 Deployment Dashboard and information about other online ISP resources. The ISP website has been long criticized for being difficult to use and find information. The addition of the Dashboard and other changes to the website are our attempt to address some of these concerns and provide an easier way to find information especially as we undertake NG9-1-1 deployment. The Dashboard includes a status map of all Virginia PSAPs as it relates to the deployment of NG9-1-1, including the total amount of funding requested by the localities and awards approved by the 9-1-1 Services Board. During this webinar, Lyle Hornbaker, Regional Coordinator for Region 5, will navigate through the dashboard and provide tips on how to more effectively utilize the ISP website. 12 June 2019. Recording not currently available. Please see the Virginia Next Generation 9-1-1 Dashboard and Resources Update webinar recording from 16 June 2021. Topic: PSAP Boundary Development Tools and Process RecommendationDescription: This webinar will be presented by Geospatial Program Manager Matt Gerike and VGIN Coordinator Joe Sewash. With the release of the PSAP boundary development tools and PSAP boundary segment compilation guidelines on the VGIN Clearinghouse in March, this webinar demonstrates the development tools, explains the process model, and discusses methods, tools, and resources available for you as you work to complete PSAP boundary segments with your neighbors. 15 May 2019. Recording available at: https://www.youtube.com/watch?v=kI-1DkUQF9Q&feature=youtu.beTopic: NG9-1-1 Data Preparation - Utilizing VITA's GIS Data Report Card ToolDescription: This webinar, presented by VGIN Coordinator Joe Sewash, Geospatial Program Manager Matt Gerike, and Geospatial Analyst Kenny Brevard will provide an overview of the first version of the tools that were released on March 25, 2019. These tools will allow localities to validate their GIS data against the report card rules, the MSAG and ALI checks used in previous report cards, and the analysis listed in the NG9-1-1 migration proposal document. We will also discuss the purpose of the tools, input requirements, initial configuration, how to run them, and how to make sense of your results. 10 April 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224495Topic: NG9-1-1 PSAP Boundary Best Practice WebinarDescription: During the months of November and December, VITA ISP staff hosted regional training sessions about best practices for PSAP boundaries as they relate to NG9-1-1. These sessions were well attended and very interactive, therefore we feel the need to do a recap and allow those that may have missed the training to attend a makeup session. 30 January 2019. Recording not currently available. Please see the PSAP Boundary Development Tools and Process Recommendation webinar recording from 15 May 2019.Topic: NG9-1-1 GIS Overview for ContractorsDescription: The Commonwealth of Virginia has started its migration to next generation 9-1-1 (NG9-1-1). This migration means that there will be a much greater reliance on geographic information (GIS) to locate and route 9-1-1 calls. VITA ISP has conducted an assessment of current local GIS data and provided each locality with a report. Some of the data from this report has also been included in the localities migration proposal, which identifies what data issues need to be resolved before the locality can migrate to NG9-1-1. Several localities in Virginia utilize a contractor to maintain their GIS data. This webinar is intended for those contractors to review the data in the report, what is included in the migration proposal and how they may be called on to assist the localities they serve. It will still ultimately be up to each locality to determine whether they engage a contractor for assistance, but it is important for the contractor community to understand what is happening and have an opportunity to ask questions about the intent and goals. This webinar will provide such an opportunity. 22 August 2018. Recording not currently available. Please contact us at NG911GIS@vdem.virginia.gov if you are interested in this content.

Curriculum Mapping Software Market Outlook

The global curriculum mapping software market size was valued at USD 1.2 billion in 2023 and is expected to reach an estimated USD 3.8 billion by 2032, growing at a CAGR of 13.2% during the forecast period from 2024 to 2032. This significant growth can be attributed to the increasing emphasis on personalized learning experiences, the necessity for compliance with educational standards, and the growing adoption of digital tools in the education sector.

One of the primary growth factors for the curriculum mapping software market is the rising demand for personalized and adaptive learning solutions. Educational institutions are increasingly leveraging technology to design curricula that cater to individual student needs. This shift not only enhances the learning experience but also improves student performance and engagement. Additionally, the ability of curriculum mapping software to help educators identify gaps in the curriculum and align teaching methods with learning objectives contributes significantly to its adoption.

Another driving force behind the market's growth is the increased focus on compliance with educational standards and accreditation requirements. Curriculum mapping software allows institutions to systematically design, implement, and review curricula to ensure they meet the necessary standards and regulations. This capability is particularly crucial for higher education institutions seeking accreditation or re-accreditation, as it provides a clear, organized, and accessible record of curriculum alignment and effectiveness.

The growing integration of data analytics and artificial intelligence in curriculum mapping software also plays a crucial role in market expansion. These technologies enable the software to offer advanced analytics, predictive modeling, and insights, which help educators make informed decisions about curriculum design and instruction. The ability to analyze student performance data and predict learning outcomes can facilitate proactive interventions, thus improving the overall educational experience.

Regionally, North America is expected to dominate the market due to the early adoption of advanced educational technologies, the presence of prominent market players, and substantial government funding for educational innovations. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period. Countries such as China, India, and Japan are investing heavily in educational technology to enhance their education systems, driven by the increasing demand for skilled professionals and the need for modernized educational infrastructure.

In addition to curriculum mapping software, educational institutions are increasingly turning to Gradebook Software to streamline their assessment and grading processes. Gradebook Software provides educators with a comprehensive platform to manage student grades, track academic progress, and generate detailed reports. This software not only simplifies the grading process but also enhances transparency and communication between teachers, students, and parents. By integrating Gradebook Software with curriculum mapping tools, institutions can create a cohesive educational ecosystem that supports personalized learning and data-driven decision-making. The growing demand for efficient and user-friendly grading solutions is driving the adoption of Gradebook Software across various educational settings.

Component Analysis

The curriculum mapping software market can be segmented based on components into software and services. The software segment accounts for the largest share of the market, driven by the increasing adoption of digital platforms for curriculum design and management. Educational institutions are recognizing the benefits of using specialized software to streamline and enhance the curriculum development process. This software facilitates the creation, organization, and assessment of curricula, providing educators with tools to align instructional practices with learning objectives effectively.

Within the software segment, the market is further divided into cloud-based and on-premises solutions. Cloud-based curriculum mapping software is gaining significant traction due to its scalability, flexibility, and cost-effectiveness. These solutions allow institutions to access the software from anywhere, at any time, and often come with automatic updates

Supplemental material about the paper filtering in each phase of the systematic mapping study

GIS-NET Public was developed to provide the Public with spatial tools and planning and zoning information in the UNINCORPORATED areas of Los Angeles County. It was developed by the Los Angeles County Department of Regional Planning's (DRP) GIS Section. This application replaces a previous application that was in service from January of 2013 to September 2018. The application uses Geocortex Essentials software from Latitude Geographics. Comments about the application can be sent to gis@planning.lacounty.govThe Department of Regional Planning performs all land use planning functions for the unincorporated areas of Los Angeles County. Our services include long range planning, land development counseling, project/case intake and processing, environmental review and zoning enforcement for each of our County unincorporated communities.What is an UNINCORPORATED area of Los Angeles County?There are 88 incorporated cities within Los Angeles County, each with its own city council. The areas that are NOT part of these cities are considered to be UNINCORPORATED County territory. More than 65 percent of Los Angeles County is unincorporated. For the 1 million people living in these areas, the Board of Supervisors and County Departments provide the municipal services.

This data corresponds to the information extracted from the studies of a systematic mapping in the adaptive monitoring topic, used for answering the research questions of the review. Data is grouped by study (or resource) and by approach.

The Mapping Software market has evolved significantly over the past decade, emerging as a vital tool across various industries such as transportation, real estate, urban planning, and logistics. As organizations strive for enhanced efficiency and competitive advantage, mapping software provides comprehensive solutio

This dataset contains the final output from the Step 2 analysis.DataStep One of this analysis primarily relies on travel data acquired through Replica.10 This dataset is produced by an activity-based model, calibrated locally with ground truth data from a diverse set of third-party source data such as mobile location data, consumer marketing data, geographic and land use data, credit card transaction data, built environment, and economic activity. Trip data provided by Replica platform includes information such as origin, destination, land use, trip purpose, and socio-demographic of the trip taker. Step Two of this analysis relies on a variety of parcel-level data from member jurisdictions, MWCOG, and DOE charging station data. This data includes existing charging stations11, Equity Emphasis Areas (EEA), Alternative Fuel Corridors (AFCs), transit stations, multifamily housing, EV Charging Justice40 Map, and MWCOG Regional Activity Centers (RACs).Step OneStep One relies primarily on travel data at the census block group level. Census block groups are scored based on trip characteristics that end within that group. Trip characteristics considered in Step One include:Trip purposeTrip lengthDwelling time (30 to 60 minutes, 60 to 120 minutes, and greater than 120 minutes)Income of tripTrips originating from multifamily housingTrips originating from equity emphasis areasThree different trip characteristic scenarios were completed in Step One, outlined in below.Prioritizing DCFCs with High Utilization: This scenario weights trips taken by people with higher incomes more heavily. Because people with higher incomes are also more likely to be homeowners with access to home charging, this scenario would focus on building out DCFCs to provide opportunities for public charging that would help serve a larger number of vehicles more quickly. Scoring adjustments in Step two provide a check on recommending an overbuilding of DCFCs in wealthier areas that already have ample access to public charging.Prioritizing Level 2 Chargers with Equity Focus: DCFCs require higher upfront costs for equipment, installation, and potential utility upgrades that may be needed to accommodate higher powered charging infrastructure. The cost of the electricity at the point of purchase is also higher, which can cause some service providers to cite economic infeasibility when deciding whether to cite DCFCs in communities with less EVs and lower utilization. Most Level 2 charging infrastructure will not require grid or electrical service upgrades, and the projects will have lower costs across other factors (e.g., equipment costs, electricity pricing for customers). Prioritizing Level 2 charging will mean there are fewer barriers to entry for a jurisdiction or project team looking to build out their charging network in EEAs.Prioritizing DCFCs for Multi-Family Housing: Individuals living in multi-family housing that don’t have a dedicated parking spot or reliable access to at-home charging, opportunity charging with DCFCs and workplace charging are two available options. Multi-family residents are more likely to use DCFC stations. Establishing DCFC charging hubs near higher concentrations of multi-family housing developments could provide an attractive and highly utilized alternative to on-site charging for buildings where it is challenging to install and maintain charging infrastructure.Each CBG is scored based on the percentage of regional trips it receives meeting the criteria. The final Step 1 analysis assigns each CBG in the study area a score of 1 to 6. The higher the CBG score, the more traffic a CBG experiences. For example, if a CBG with a score of 1 has a low number of trips starting or ending there, whereas a CBG with a score of 6 has a very high number of trips starting or ending there.Step TwoOnce the census block groups have been scored, individual parcels within high-scoring census block groups are evaluated based on characteristics that make that parcel more or less desirable for charging infrastructure. Those characteristics, called proximity score modifiers, include a parcel’s distance to existing charging stations, distance to multi-family housing, distance to highway on- and off-ramps, proximity to environmental justice communities, and distance to park-and-ride locations. These proximity score modifiers have been selected for the following reasons:Distance to existing charging stations. Locations that are close to existing public chargers have already begun to be built out and may have less demand.Distance to MFH. Residents of MFH typically lack access to home charging and will rely on public infrastructure to meet charging needs.Distance to highway on-ramp or off-ramp. Sites located near highway ramps are likely to attract EV drivers who are making longer trips, typically needing DCFC.Location in or near an EEA. Ensuring the benefits of EVs are spread equitably in the region is a priority. Providing access to charging infrastructure in or near EEAs can help remove barriers to EV adoption.Distance to park-and-ride locations. The distance from potential sites to the nearest public transportation stop with park-and-ride lots is calculated to determine which sites will be most useful in enabling more sustainable first and last miles of multimodal trips. Charging locations near transit stops could benefit EV ride-sharing companies or commuters that use a combination of personal vehicles and mass transit.Each proximity score modifier can increase or decrease a parcel’s overall score. If a parcel is not located near any proximity score modifier, their final score will not be influenced by these characteristics. Proximity score modifiers and their associated point values are as follows:Within ¼ mile of a Park-and-Ride Location - Parcel score increases by 1Within ¼ mile of MFH - Parcel score increases by 1Within ¼ mile of an EEA - Parcel score increases by 1Within ½ mile of Existing Level 2 Charging Stations - Parcel score decreases by up to 2 pointsWithin ½ mile of Existing DCFC Stations - Parcel score decreases by up to 4 pointsThese factors are assessed with GIS software and compiled to modify the parcel’s charging demand score. A parcel’s final score in Step 2 is determined by the following formula:Parcel Score = [Step 1 Census Block Group Score] + [Proximity Score Modifier Total]Parcels that are better suited for charging will score higher than parcels less suitable for charging. Each high scoring parcel should be further reviewed to determine suitability for public EV charging stations such as parcel size, parking availability, facility access, potential site host partnerships, and electric utility service capacity. Local knowledge is key to understanding results.

This study aims to investigate and characterize the factors that affect Developer Experience (DX) in Software Ecosystems (SECO). To do so, we reviewed the existing literature on scientific databases and digital libraries to map and analyze the state-of-the-art of DX in software development and SECO. As the main contribution, we provide a set of factors to help organizations, teams, individual software developers, and researchers to have a better understanding of the topic and create a better experience that allows them to promote engagement and other benefits when developing artifacts in SECO or elsewhere areas of Software Engineering.

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.

BackgroundToxoplasma gondii, a cosmopolitan protozoan parasite causes toxoplasmosis in humans and many species of domestic and wild animals. T. gondii instigates significant economic losses in sheep and goat farming industry and can lead to abortion, stillbirth, congenital malformations and neonatal losses. The objective of this protocol is to evaluate worldwide seroprevalence of T. gondii exposure in goats using Bayesian hierarchical meta-analysis and geographic information system (GIS).MethodsA comprehensive literature search will be conducted using search engines, including Web of Science, ScienceDirect, Scopus, PubMed, ProQuest, EMBASE, PROSPERO Register and, Google Scholar without date and language restrictions. The authors search for cross-sectional studies that determine the seroprevalence of T. gondii in goats. Two reviewers will independently screen, selected studies; also, they will extract data, and assess the risk of bias. In case(s) of disagreement, a consensus will be reached with the help of a third author. The Bayesian hierarchical meta-analysis will use to estimate country and worldwide true seroprevalence of T. gondii, which is consist of the sensitivity and specificity of the applied serological assays. The obtained data will be used to identify country-level risk factors associated with T. gondii exposure using GIS in the ArcGIS software.DiscussionThe systematic review produced from this protocol will provide the true prevalence rate and spatial distribution T. gondii exposure in goats both regionally and globally using Bayesian hierarchical and GIS analysis.Systematic review registrationPROSPERO registration number: CRD42020107928.

Zoning district boundaries by type and classification. Chicago is divided into zoning districts that regulate land use activities across the city. Data is based on the Chicago Zoning Ordinance and Land Use Ordinance http://bit.ly/9eqawi. Zoning Types are defined in this ordinance. To view or use these files, compression software and special GIS software, such as ESRI ArcGIS, is required. For additional information about business uses, review the License/Zoning Reference (LZR) Guide http://bit.ly/vvGzne, which is based on the Municipal Code and is intended to assist business owners in determining the proper zoning district and primary business license for specific business types. Update Frequency: Data is updated monthly. Related Applications: Zoning Map https://gisapps.cityofchicago.org/zoning/

This is a vector tile service of the fine scale vegetation and habitat map, to be used in web maps and GIS software packages. It is mean to be used in conjunction with the vector tile service that provides labels for each polygon. There is an additional vector tile service that provides solid colored polygons for the vegetation map if hollow outlines are not desired. The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8). The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

Explore the historical Whois records related to process-mapping-software.com (Domain). Get insights into ownership history and changes over time.