No description provided by data sponsor

San Francisco Planning Department's Planning Areas.

the layout planning of residential community has always been of concern

Procedural Planning

## Overview

Procedural Planning is a dataset for object detection tasks - it contains BIM Elements annotations for 2,283 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

DSM2 studies used for the DSM2 Planning Study Training in the DSM2 Learning Series

The PDB is a database of U.S. housing, demographic, socioeconomic and operational statistics based on select 2010 Decennial Census and select 5-year American Community Survey (ACS) estimates. Data are provided at the census tract level of geography. These data can be used for many purposes, including survey field operations planning.

The global urban and rural planning and design market is experiencing robust growth, driven by rapid urbanization, increasing infrastructure development needs, and a growing focus on sustainable development practices. The market, segmented by type (Master and Zoning Planning, Regulatory Detailed Planning, Constructive Detailed Planning, and Others) and application (Urban Planning and Rural Planning), is projected to reach a substantial value in the coming years. The CAGR, while not explicitly stated, can be reasonably estimated based on industry trends and comparable sectors to be around 6-8% for the forecast period (2025-2033). This growth is fueled by significant government investments in infrastructure projects globally, particularly in emerging economies experiencing rapid population growth and urbanization. Furthermore, the increasing adoption of advanced technologies like GIS, BIM, and AI for planning and design is further enhancing efficiency and accuracy, driving market expansion. Major players are actively involved in mergers and acquisitions, strategic partnerships, and technological advancements to consolidate their market position and gain a competitive edge. The market is witnessing a rise in demand for sustainable and resilient urban planning solutions, focusing on green infrastructure, smart cities initiatives, and climate change adaptation strategies. While the market presents significant opportunities, several challenges exist. Regulatory complexities and lengthy approval processes in many regions can hinder project timelines and increase costs. Furthermore, the availability of skilled professionals and qualified urban planners remains a bottleneck in certain regions. Economic fluctuations and the impact of global events also pose risks to market growth. However, the long-term outlook remains positive, driven by the continuous need for effective urban and rural planning to address the challenges of urbanization and ensure sustainable development. The regional distribution of the market is expected to be heavily influenced by the level of infrastructure spending and economic development in each region, with Asia-Pacific and North America likely to dominate the market share in the coming years due to robust infrastructural projects and investments in smart city development. Competitive analysis reveals a diverse landscape with both established international firms and regional players, creating a dynamic and evolving market structure.

To illustrate the outlines of the 18 Districts for Philadelphia2035 District Plans

Character area plans are created collaboratively with residents and businesses of an area and serve as a flexible, long-term guide for a neighborhood’s future. When accepted by Council, the plans provide guidance in design-related matters for City’s investments related to infrastructure, landscaping, transportation and housing.Site is Google Translate enabled.

1. The urban and regional development statistics compilation includes regular statistical data on urban and regional development published by different agencies. 2. Data source: Ministry of the Interior (https://goo.gl/x69AK6)

This container has shapefiles for planning information throughout the City of Gillette. The shapefiles are Annexation, City Zoning, Land use Comp, Neighborhood, and Zone Lot. Please contact the City of Gillette GIS Division at (307) 686-5364 or gisadmin@gillettewy.gov for more information. last updated: 2025-07-20 17:08:14.947648

Planning units provide a more detailed geographic basis for identifying and assessing water quality improvement activities. A planning unit is either an individual large tributary basin or a group of smaller adjacent tributary basins with similar characteristics. Planning units help organize information and management strategies around prominent watershed characteristics.

Dataset

This dataset was created by Adrian Hidalgo

Released under CC0: Public Domain

Contents

This dataset provides processed and normalized/standardized indices for the management tool group 'Scenario Planning', including related concepts like Scenario Analysis and Contingency Planning. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Scenario Planning dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "scenario planning" + "scenario analysis" + "contingency planning" + "scenario planning business". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Scenario Planning + Scenario Analysis + Contingency Planning + Scenario and Contingency Planning. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Scenario Planning-related keywords [("scenario planning" OR ...) AND ("management" OR ...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Scenario Planning Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Scenario Planning (1993, 1999, 2000); Scenario and Contingency Planning (2004, 2006, 2008, 2010, 2012, 2014, 2017); Scenario Analysis and Contingency Planning (2022). Processing: Semantic Grouping: Data points across the different naming conventions were treated as a single conceptual series. Normalization: Combined series normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Scenario Planning (1993, 1999, 2000); Scenario and Contingency Planning (2004, 2006, 2008, 2010, 2012, 2014, 2017); Scenario Analysis and Contingency Planning (2022). Processing: Semantic Grouping: Data points treated as a single conceptual series. Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Scenario Planning dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

Planning Jurisdictions in Wake County, NC. Planning Jurisdictions are areas over which municipalities and the County have planning authority (either official ETJ and/or corporate limits). This dataset is updated monthly and is maintained by the Wake County Planning Department.GIS metadata is available here.

This data can also be viewed at https://data-housinggovie.opendata.arcgis.com/

Thank you for visiting the dataset for Planning Board Meeting Agendas for the year 2021. Scroll to the bottom of this page to download pdf's of the agenda you wish to review.The Jersey City Planning Board typically meets every other Tuesday. The Board is comprised of Jersey City Residents, Elected Officials (or designee), and a municipal employee. The Board votes on development and subdivision applications that are proposed throughout the City.Should you have any questions please reach out to the Division of City Planning at 201-547-5010 or email cityplanning@jcnj.org.During the state of emergency, meetings will be held virtually. Please review agendas for more information on how to access plans and virtual meetings.

Scientific Data Management (SDM) Program shares and manages scientific and scientific program information systems in ways that support the mission and business of the NWFSC. We strive to bring quality information, in the right form, to the right people at the right time to support necessary decisions and generate ideas. Multi-FMC annual project planning (for budget, people, and operational costs) and data set tracking (for data entry to feed InPort/NCEI/Data.gov) TEST CASE TWO.