The General Order detailing RPD's mission statement.

Locations of community comments on various safety topics for the Vision Zero program in Arlington County. The data also includes a topic type, the comments, and the year the comments were given.Contact: Department of Environmental ServicesData Accessibility: Publicly AvailableUpdate Frequency: AnnuallyLast Revision Date: 1/17/2024Creation Date: 1/17/2024Feature Dataset Name: DES_TrafficLayer Name: VisionZero_CommunityComment_pnt

The Remote Sensing Image Processing Platform market is experiencing robust growth, projected to reach $2542 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 6.1% from 2025 to 2033. This expansion is driven by several key factors. The increasing availability of high-resolution satellite imagery, coupled with advancements in cloud computing and artificial intelligence (AI), are enabling more efficient and sophisticated image analysis. Furthermore, growing demand for precise geospatial data across various sectors, including agriculture, urban planning, environmental monitoring, and defense, fuels market growth. The integration of AI and machine learning algorithms allows for automated feature extraction, object detection, and classification, significantly improving the speed and accuracy of image processing, thereby lowering operational costs and increasing efficiency. This is leading to wider adoption across industries, expanding the overall market size. The competitive landscape is characterized by a mix of established players and emerging technology providers. Key players like ClearSKY Vision, ESRI, Hexagon, and others are investing heavily in research and development to enhance their platforms' capabilities, offering advanced analytics, improved user interfaces, and cloud-based solutions. Market segmentation, while not explicitly detailed, likely includes variations based on software type (e.g., cloud-based vs. on-premise), application (e.g., agriculture, defense), and licensing models (e.g., subscription vs. perpetual). While restraints may include the high cost of entry for some advanced platforms and the need for specialized expertise, the overall market trajectory indicates a significant and sustained period of growth driven by technological innovation and expanding industry demand.

The Regional Policy Statement shows the broad direction for managing Northland's natural and physical resources. These include land, water, air, soil, minerals, plants, animals and all built structures in the Northland region.The Regional Policy Statement map identifies outstanding natural landscapes, outstanding natural features, areas of high or outstanding natural character and the delineates the coastal environment.

The Geospatial Data Fusion market is experiencing robust growth, driven by increasing demand for precise location intelligence across diverse sectors. The market, valued at approximately $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. The proliferation of Earth observation technologies, including satellite imagery and sensor data, provides a massive influx of raw data, necessitating sophisticated fusion techniques for actionable insights. Simultaneously, advancements in artificial intelligence (AI), particularly in computer vision and machine learning, are enhancing the accuracy and speed of data processing and analysis. The military and security sectors are significant contributors to market growth, utilizing geospatial data fusion for strategic planning, threat assessment, and real-time situational awareness. Furthermore, the rising adoption of cloud-based solutions (SaaS and PaaS) is streamlining data access, storage, and processing, further boosting market adoption. The market is segmented by application (Earth Observation and Space Applications, Computer Vision, Military, Security, Other) and deployment type (SaaS, PaaS), with SaaS currently dominating due to its accessibility and scalability. However, the market also faces some challenges. The high cost of data acquisition and processing can be a barrier to entry for smaller organizations. Data integration complexities, varying data formats, and ensuring data security are also crucial considerations. Despite these constraints, the market’s growth trajectory is expected to remain positive, propelled by continuous technological advancements, the increasing availability of geospatial data, and the growing need for precise location-based insights across various industries, ranging from urban planning and environmental monitoring to precision agriculture and disaster response. The competitive landscape features established players like Esri and emerging innovative companies like Geo Owl and Magellium, fostering healthy competition and driving innovation within the market.

The Saudi Arabian geospatial analytics market, valued at $400 million in 2025, is poised for significant growth, exhibiting a Compound Annual Growth Rate (CAGR) of 9.22% from 2025 to 2033. This expansion is driven by several key factors. Firstly, substantial investments in infrastructure development, including smart city initiatives and digital transformation across various sectors, are fueling the demand for sophisticated geospatial analytics solutions. Secondly, the Kingdom's strategic focus on Vision 2030, which emphasizes diversification and technological advancement, is creating a favorable environment for the adoption of geospatial technologies across sectors such as agriculture, utilities, defense, and real estate. The increasing availability of high-resolution satellite imagery, coupled with advancements in data analytics and artificial intelligence (AI), further enhances the market's growth trajectory. Government initiatives promoting data sharing and open data platforms are also playing a crucial role. Segmentation reveals that network analysis and geovisualization are experiencing the fastest growth, driven by their applications in urban planning, resource management, and emergency response. Key players, including established technology giants like Microsoft and Esri, as well as specialized geospatial firms, are actively competing in this dynamic market, contributing to innovation and service diversification. Despite the promising outlook, challenges remain. Data security and privacy concerns related to handling sensitive geospatial data pose a significant restraint. Furthermore, the lack of skilled professionals proficient in geospatial analytics and data interpretation could hinder market growth in the short term. Nevertheless, ongoing investments in education and training programs should mitigate this issue. The overall market landscape indicates substantial potential for growth, particularly in leveraging geospatial analytics for sustainable development and effective resource allocation across Saudi Arabia's diverse sectors. The forecast period, spanning from 2025 to 2033, projects substantial market expansion, driven by consistent technological innovation and governmental support for digital transformation. Recent developments include: May 2023: Microsoft introduced three new functions for geospatial analysis in Azure Data Explorer, geo_point_buffer, geo_line_buffer, and geo_polygon_buffer. These functions allow users to create polygonal buffers around geospatial points, lines, or polygons, respectively, and return the resulting geometry. Users can use these functions to perform spatial operations such as intersection, containment, distance, or proximity on user geospatial data or to visualize data on maps., October 2022: ROSHN, the Kingdom of Saudi Arabia's nationwide real estate developer, backed by the government's Public Investment Fund (PIF), supported government efforts to improve homeownership rates while delivering sophisticated living standards. The Saudi Arabia designer built communities that looked to the nation's heritage and evolving resident aspirations. To support its vision and ongoing regional projects, ROSHN signed a memorandum of understanding (MOU) with Esri, the global player in location intelligence., . Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Notable trends are: Geovisualization is Expected to Hold Significant Share of the Market.

DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the Hub application. To make changes to this site, please visit https://hub.arcgis.com/admin/

Through collaboration this is a dataset of location of Vision Zero projects to mitigate public safety

Feature service for Hub annotations. DO NOT DELETE THIS SERVICE. It stores the public annotations (comments) for all Hub items in your organization.

This layer shows six different types of disability. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of population with a disability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B18101, B18102, B18103, B18104, B18105, B18106, B18107, C18108 (Not all lines of these ACS tables are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

User-submitted safety concerns currently updating from the Vision Zero Safety Concerns application on boston.gov. Published June 2022.

Areas of High and Outstanding Natural Character as agreed by all parties concerned, to be used in the Operative Regional Policy Statement.

Locations of the Outstanding Natural Landscapes throughout Northland, as agreed by all concerned parties for use in the Operative Regional Policy Statement

Attachment regarding request by Walter Lewis for a Conditional Use B-1 Business Permit for an indoor storage facility for boats, recreational vehicles, and other vehicular and self storage uses on approximately 5.35 acres located at the corner of US 64 E and Bob Horton Rd (SR 1744), New Hope Township.

The Complete FY 2021-2025 Vision Zero Action Plan as presented to Council

The Vision Zero Safety data comes from a web-based application developed to allow the public to communicate the real and perceived dangers along the roadway from the perspective of either a pedestrian, bicyclist or motorist. The data is captured from a site visitor who can click or tap on a location to report a transportation hazard. Vision Zero is a part of Mayor Bowser’s response to the US Department of Transportation’s Mayor’s Challenge for Safer People and Safer Streets, which aims to improve pedestrian and bicycle transportation safety by showcasing effective local actions, empowering local leaders to take action, and promoting partnerships to advance pedestrian and bicycle safety. Vision Zero requires an all-hands-on-deck approach. More than 20 District government agencies are engaged in the Vision Zero Initiative, including DDOT, Department of Public Works, the Deputy Mayor for Health and Human Services, Metropolitan Police Department, DC Taxi Cab Commission, the Department of Motor Vehicles, the DC Office on Aging, DC Public Schools, Fire and Emergency Medical Services, Homeland Security and Management, Office of Unified Communications, Department of Health, the Office of the Attorney General, Office of the Chief Technology Officer, Office of Disability Rights, Office of Planning, Office of the City Administrator, Office of the State Superintendent of Education, the Deputy Mayor for Education, Office of Policy and Legislative Affairs, and the Deputy Mayor for Planning and Economic Development. Contact the Vision Zero team at vision.zero@dc.gov.Please note that this map is not DC's 311 service request system. Department of Transportation (DDOT) staff will investigate the concerns identified on this map and if necessary, submit a 311 request on behalf of the site visitor. Users always have the option to submit a separate 311 service request at https://311.dc.gov/.

OPEN DATA POLICY PURPOSE APPLICABILITY POLICY STATEMENT DEFINITIONS PROGRAM GOALS OPEN DATA PROGRAM

This model detects pavement cracks and potholes in drone imagery. Such deterioration of road surfaces may be caused by poor construction, heavy load or weather factors. This negatively affects road safety, driving comfort, and the wear and tear of vehicles. Civic authorities need to accurately identify these cracks and perform repair work. If these cracks are not repaired at an early stage, the cost of repair escalates quickly.Traditionally, inspection of road surface is done by human inspectors or by using sophisticated machines which are accurate but very expensive. This model can automate the pavement inspection process using drone imagery, and enable better monitoring and maintenance of roads.Using the modelFollow the guide to use the model. To use this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. This is a computer vision model and cannot be fine-tuned.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.InputThis model is expected to work on high resolution drone imagery in the form of a raster, mosaic dataset, or image service. The preferred cell size is under 2 centimeters per pixel.OutputFeature class containing detected pavement cracks.Applicable geographiesThe model is expected to work globally.Model architectureThe model is implemented using computer vision techniques. Accuracy metricsThis model is implemented using computer vision techniques and due to non availability of ground truth data, accuracy metrics are not available. The model cannot be fine-tuned. Sample resultsHere are a few results from the model.

Vision Zero originated in Sweden in 1997. Now a phenomenon throughout North America, its key message is, "no loss of life is acceptable." Vision Zero’s goal is to reduce – and finally end – injuries or deaths caused by motor vehicle collisions. This goal is based on everyone sharing the responsibility of road safety, whether you're involved in the road system, vehicle manufacturers, or road users.Vision Zero in PeelAny injury or death on a Peel road is unacceptable. Vision Zero will create safer roads in Peel for drivers, cyclists and pedestrians. Vision Zero will help stop people from getting hurt or dying in motor vehicle collisions in Peel.Our vision: Zero fatal and injury collisions for all road usersOur goal: 10% reduction in fatal and injury collisionsCouncil adopted the Vision Zero framework (PDF) in December 2017.Together with our partners, we've:Reviewed traffic collision information in PeelPinpointed key problem areasSet actions to make improvements in each key problem areaFor more information read the Region’s full Vision Zero Road Safety Strategic Plan (PDF).

The CCC layer was prepared by Public Works as part of the development of the County's Vision Zero Action Plan in partnership with the Los Angeles County Department of Public Health and in collaboration with community stakeholders. A Collision Concentration Corridor is defined as any half-mile roadway segment that contained three or more fatal or severe injury collisions between January 1, 2013, and December 31, 2017. A priority score was developed for each segment by first totaling the number of fatal and severe injury collisions that occurred on that segment, and then weighting for fatal collisions, pedestrian or bicycle-involved collisions, and collisions occuring in disadvantaged areas. A disadvantaged area is defined as a community in the lowest quartile of the California Healthy Places Index. Collision Concentration Corridor Priority Score = (#_of_Fatal_& Severe_Injury_Collisions + 0.5*#_of_Fatal_Collisions_that_Involved_Any_Type_of_Travel_Mode + 0.25*#_of_Fatal_&_Severe_Injury_Collisions_that_Involved_Vulnerable_Road_Users + 0.25*#_of_Fatal_and_Severe_Injury_Collisions_that_occurred_in_a_Disadvantaged Community) / Segment_Length*The minimum segment legnth was assumed to be 0.5 miles