AndreaBozzo/ceres-open-data-index huggingface dataset:
Impervious Surface 2019

<div style='text-align:Left;'><div><div><p><span>The dataset contains basemap features that are typically classified as impervious surface, captured in 2019. Some examples of Impervious Surfaces include: Airport Taxiways, Helipads, Outdoor Building Stairs, Buildings, Sidewalks, Roads, Alleys, Driveways, and Swimming Pools.</span></p></div></div></div>

Tags: district-of-columbia, impervious-surface, octo, stormwater, washington-dc, water
Organization: District of Columbia
Impervious Surface 2013

<div style='text-align:Left;'><div><div><p><span>The dataset contains basemap features that are typically classified as impervious surface, captured in 2013. Some examples of Impervious Surfaces include: Airport Taxiways, Helipads, Outdoor Building Stairs, Buildings, Sidewalks, Roads, Alleys, Driveways, and Swimming Pools.</span></p></div></div></div>

Tags: dc-gis, doee, environment, impervious, storm-water, surface, washington-dc
Organization: District of Columbia
Impervious Surface 2015

<div style='text-align:Left;'><div><div><p><span>The dataset contains basemap (planimetric) features that are typically classified as impervious surface captured in 2015., created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Some examples of Impervious Surfaces include: Airport Taxiways, Helipads, Outdoor Building Stairs, Buildings, Sidewalks, Roads, Alleys, Driveways, and Swimming Pools.</span></p></div></div></div>

Tags: district-of-columbia, doee, environment, impervious, runoff, storm-water, surface, washington-dc
Organization: District of Columbia
Impervious Surface 2010

<div style='text-align:Left;'><div><div><p><span>The dataset contains basemap features that are typically classified as impervious surface, captured in 2010. Some examples of Impervious Surfaces include: Airport Taxiways, Helipads, Outdoor Building Stairs, Buildings, Sidewalks, Roads, Alleys, Driveways, and Swimming Pools.</span></p></div></div></div>

Tags: district-of-columbia, impervious-surface, octo, stormwater, washington-dc, water
Organization: District of Columbia
DC Trees

<div style='text-align:Left;'><div><div><p><span>DC 2022 LiDAR was used and processed using the “Extract Trees using Cluster Analysis” script which is included as part of Esri’s 3D Basemap solution. All LiDAR-derived trees within 2 meters of a Urban Forestry Division tree were removed as being duplicates.</span></p><p><span>Tree diameter (DBH, in inches) was estimated for the LiDAR-derived trees from calculated tree height (in feet) based on the equation: DBH = 0.4003*height - 1.9557. This equation was derived from a statistical analysis of a detailed park inventory tree data set and has an R^2 = 0.7418.</span></p><p><span>Extreme outliers were also modified, with any DBH larger than 80 inches being converted to a DBH of 80 inches.</span></p><p><span>The combined data set was processed using the USDA Forest Service i-Tree eco software, where structure and environmental benefits were estimated.</span></p></div></div></div>

Tags: dc, dc-trees, environmental-benefits, estimate-trees, extracted-trees, urban-forestry, washington
Organization: District of Columbia
Urban Tree Canopy by DC Owned Property in 2020

<div style='text-align:Left;'><div><div><p><span>These data represent DC Owned Lots in Washington, DC. Urban tree canopy (UTC) and possible planting area (PPA) metrics have been calculated for DC Owned Lots within the study area. UTC results provided in vector format with attribute fields (area/percent metrics/percent change metrics) for each land cover class and UTC type (UTC, PPA, Unsuitable UTC, UTC Change).</span></p><p><span /></p></div></div></div>

Tags: dc, ddot, district-of-columbia, environment, land-cover, naip, possible-planting-area, ppa, tree-canopy, tree-cover, ufd, urban-forestry, utc, washington-dc
Organization: District of Columbia
Urban Tree Canopy by DC Boundary in 2020

<div style='text-align:Left;'><div><div><p><span>These data represent DC Boundary in Washington, DC. Urban tree canopy (UTC) and possible planting area (PPA) metrics have been calculated for DC Boundary within the study area. UTC results provided in vector format with attribute fields (area/percent metrics/percent change metrics) for each land cover class and UTC type (UTC, PPA, Unsuitable UTC, UTC Change).</span></p></div></div></div>

Tags: dc, ddot, district-of-columbia, environment, land-cover, naip, possible-planting-area, ppa, tree-canopy, tree-cover, ufd, urban-forestry, utc, washington-dc
Organization: District of Columbia
Urban Tree Canopy by Census Block Group in 2015

<div style='text-align:Left;'><div><div><p><span>Polygons of Urban Tree Canopy in Washington, DC in 2015. These data represent detailed urban tree canopy cover in Washington, D.C. The data were derived using remote sensing technologies on aerial imagery from the National Agriculture Imagery Program (NAIP), flown in July 2015, and aerial imagery and LiDAR data from Sanborn and the DC Office of the Chief Technology Officer, flown in April 2015.</span></p></div></div></div>

Tags: census-block-group, dc, ddot, district-of-columbia, environment, land-cover, naip, possible-planting-area, ppa, tree-canopy, tree-cover, ufd, urban-forestry, utc, washington-dc
Organization: District of Columbia
Urban Tree Canopy by Census Block in 2020

<div style='text-align:Left;'><div><div><p><span>These data represent Census Blocks 2020 in Washington, DC. Urban tree canopy (UTC) and possible planting area (PPA) metrics have been calculated for Census Blocks 2020 within the study area. UTC results provided in vector format with attribute fields (area/percent metrics/percent change metrics) for each land cover class and UTC type (UTC, PPA, Unsuitable UTC, UTC Change).</span></p><p><span /></p></div></div></div>

Tags: census-block, dc, ddot, district-of-columbia, environment, land-cover, naip, possible-planting-area, ppa, tree-canopy, tree-cover, ufd, urban-forestry, utc, washington-dc
Organization: District of Columbia
Urban Tree Canopy by Census Block in 2015

<div style='text-align:Left;'><div><div><p><span>Polygons of Urban Tree Canopy in Washington, DC in 2015. These data represent detailed urban tree canopy cover in Washington, D.C. The data were derived using remote sensing technologies on aerial imagery from the National Agriculture Imagery Program (NAIP), flown in July 2015, and aerial imagery and LiDAR data from Sanborn and the DC Office of the Chief Technology Officer, flown in April 2015.</span></p></div></div></div>

Tags: census-block, dc, ddot, district-of-columbia, environment, land-cover, naip, possible-planting-area, ppa, tree-canopy, tree-cover, ufd, urban-forestry, utc, washington-dc
Organization: District of Columbia
AmeriCorps Participant Demographics Dashboard

This dashboard provides visual representation for comparisons of demographic group prevalence in AmeriCorps Member/Volunteers populations to that of the greater U.S. population. The odds ratio analysis was completed by the Office of the Chief Data Officer.
Note: Toggle between dashboard pages with the arrows at the bottom of the dashboard. Pages: 1) State Results, 2) National Results, 3) Key Terms and Conditions

Tags: americorps, americorps-members, americorps-volunteer-demographics, deia, demographic-analysis, volunteers
Organization: AmeriCorps
Urban Tree Canopy by Advisory Neighborhood Commission in 2020

<div style='text-align:Left;'><div><div><p><span>These data represent Advisory Neighborhood Commissions 2013 in Washington, DC. Urban tree canopy (UTC) and possible planting area (PPA) metrics have been calculated for Advisory Neighborhood Commissions within the study area. UTC results provided in vector format with attribute fields (area/percent metrics/percent change metrics) for each land cover class and UTC type (UTC, PPA, Unsuitable UTC, UTC Change).</span></p></div></div></div>

Tags: dc, ddot, district-of-columbia, environment, land-cover, naip, possible-planting-area, ppa, tree-canopy, tree-cover, ufd, urban-forestry, utc, washington-dc
Organization: District of Columbia
Urban Tree Canopy by Advisory Neighborhood Commission in 2015

<div style='text-align:Left;'><div><div><p><span>Polygons of Urban Tree Canopy in Washington, DC in 2015. These data represent detailed urban tree canopy cover in Washington, D.C. The data were derived using remote sensing technologies on aerial imagery from the National Agriculture Imagery Program (NAIP), flown in July 2015, and aerial imagery and LiDAR data from Sanborn and the DC Office of the Chief Technology Officer, flown in April 2015.</span></p></div></div></div>

Tags: dc, ddot, district-of-columbia, environment, land-cover, naip, possible-planting-area, ppa, tree-canopy, tree-cover, ufd, urban-forestry, utc, washington-dc
Organization: District of Columbia
Urban Tree Canopy by 2019 Right of Way in 2020

<div style='text-align:Left;'><div><div><p><span>These data represent ROW 2019 in Washington, DC. Urban tree canopy (UTC) and possible planting area (PPA) metrics have been calculated for ROW 2019 within the study area. UTC results provided in vector format with attribute fields (area/percent metrics/percent change metrics) for each land cover class and UTC type (UTC, PPA, Unsuitable UTC, UTC Change).</span></p></div></div></div>

Tags: dc, ddot, district-of-columbia, environment, land-cover, naip, possible-planting-area, ppa, tree-canopy, tree-cover, ufd, urban-forestry, utc, washington-dc
Organization: District of Columbia
Urban Tree Canopy by 2015 Landuse

<div style='text-align:Left;'><div><div><p><span>Polygons of Urban Tree Canopy in Washington, DC in 2015. These data represent detailed urban tree canopy cover in Washington, D.C. The data were derived using remote sensing technologies on aerial imagery from the National Agriculture Imagery Program (NAIP), flown in July 2015, and aerial imagery and LiDAR data from Sanborn and the DC Office of the Chief Technology Officer, flown in April 2015.</span></p></div></div></div>

Tags: dc, ddot, district-of-columbia, land-cover, naip, tree-canopy, tree-cover, ufd, urban-forestry, washington-dc
Organization: District of Columbia
Urban Tree Canopy by 2006 Landuse

<div style='text-align:Left;'><div><div><p><span>Polygons of Urban Tree Canopy in Washington, DC in 2015. These data represent detailed urban tree canopy cover in Washington, D.C. The data were derived using remote sensing technologies on aerial imagery from the National Agriculture Imagery Program (NAIP), flown in July 2015, and aerial imagery and LiDAR data from Sanborn and the DC Office of the Chief Technology Officer, flown in April 2015.</span></p></div></div></div>

Tags: dc, ddot, district-of-columbia, land-cover, naip, tree-canopy, tree-cover, ufd, urban-forestry, washington-dc
Organization: District of Columbia
Urban Tree Canopy by 2011 Landuse

<div style='text-align:Left;'><div><div><p><span>Polygons of Urban Tree Canopy in Washington, DC in 2015. These data represent detailed urban tree canopy cover in Washington, D.C. The data were derived using remote sensing technologies on aerial imagery from the National Agriculture Imagery Program (NAIP), flown in July 2015, and aerial imagery and LiDAR data from Sanborn and the DC Office of the Chief Technology Officer, flown in April 2015.</span></p></div></div></div>

Tags: dc, ddot, district-of-columbia, land-cover, naip, tree-canopy, tree-cover, ufd, urban-forestry, washington-dc
Organization: District of Columbia
Urban Tree Canopy at Generalized Parcel Level in 2020

<div style='text-align:Left;'><div><div><p><span>These data represent Common Ownership Generalized in Washington, DC. Urban tree canopy (UTC) and possible planting area (PPA) metrics have been calculated for Common Ownership Generalized within the study area. UTC results provided in vector format with attribute fields (area/percent metrics/percent change metrics) for each land cover class and UTC type (UTC, PPA, Unsuitable UTC, UTC Change).</span></p></div></div></div>

Tags: dc, ddot, district-of-columbia, environment, land-cover, naip, possible-planting-area, ppa, tree-canopy, tree-cover, ufd, urban-forestry, utc, washington-dc
Organization: District of Columbia
Urban Tree Canopy by 2010 Census Block Group in 2020

<div style='text-align:Left;'><div><div><p><span>These data represent Census Block Groups 2010 in Washington, DC. Urban tree canopy (UTC) and possible planting area (PPA) metrics have been calculated for Census Block Groups 2010 within the study area. UTC results provided in vector format with attribute fields (area/percent metrics/percent change metrics) for each land cover class and UTC type (UTC, PPA, Unsuitable UTC, UTC Change).</span></p></div></div></div>

Tags: dc, ddot, district-of-columbia, environment, land-cover, naip, possible-planting-area, ppa, tree-canopy, tree-cover, ufd, urban-forestry, utc, washington-dc
Organization: District of Columbia
VPM Maintenance Requirements

The maintenance requirements for the Real Property Geodatabase are focused on
ensuring that the geodatabase design and the database itself will appropriately support the
District’s data maintenance activities for tax and record lots and associated information.
These include accurate representation of current data, structures and rules that aid quality
control, and accurate construction and maintenance of historical data.

Tags: district-of-columbia, lots, od-featured-documentation, vector-property-mapping, vpmdata, washington-dc
Organization: District of Columbia
VPM Logical Design Summary

The Vector Property Geodatabase (VPG), often referred to Real Property Geodatabase
(RPG), logical design describes the database design for storage of GIS layers identified in
the September 23, 2002, requirements document. The logical design’s goal is to satisfy
the identified requirements. The next step is to develop a physical design that implements
the geodatabase on a physical platform.
This design does not address maintenance issues. The project team has developed a
maintenance requirements document that will guide the design enhancements for the
purpose of maintenance.

Tags: district-of-columbia, lots, od-featured-documentation, vector-property-mapping, vpmdata, washington-dc
Organization: District of Columbia
School STAR Scores

<div>2018 DC School Report Card. The sum of the student group scores using all applicable STAR framework metrics. This is a number from 0 – 100 points. Overall STAR score for the school based on all applicable framework scores and student groups. Star value assigned to the school based on the STAR score.1, 2, 3, 4, or 5. </div><div><br /></div><div>Supplemental:</div><div><br /></div><div>Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings.</div><div>At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only.</div><div><br /></div><div>On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools.</div><div><br /></div><div>Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating.</div><div><br /></div><div>Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (<a href='https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide' target='_blank'>https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide</a>).</div>

Tags: charter, dcps, education, learning, osse, performance, public-school, rating, report-card, schools, score, star, student, washington-dc
Organization: District of Columbia
School STAR Framework Scores

<div>2018 DC School Report Card. A school framework is a set of metrics and weighted domains based on the school's grade configuration or school designations.  The STAR Framework contains four school frameworks: Elementary School Framework, Middle School Framework, High School Framework, and Alternative School Framework. The Elementary School Framework has two versions, one for schools with Pre-Kindergarten and one for schools without Pre-Kindergarten.</div><div><br /></div><div>Supplemental:</div><div><br /></div><div>Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings.</div><div>At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only.</div><div><br /></div><div>On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools.</div><div><br /></div><div>Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating.</div><div><br /></div><div>Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (<a href='https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide' target='_blank'>https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide</a>).</div>

Tags: charter, dcps, education, learning, osse, performance, public-school, rating, report-card, schools, score, star, student, washington-dc
Organization: District of Columbia
School STAR Enrollment

2018 DC School Report Card. School enrollment by school and student group. For enrollment the metrics are either total enrollment or percent of total enrollment. 

Supplemental:

Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings.
At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only.

On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools.

Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating.

Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (<a href='https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide' target='_blank' rel='nofollow ugc noopener noreferrer'>https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide</a>).

Tags: charter, dcps, education, learning, osse, performance, public-school, rating, report-card, schools, score, star, student, washington-dc
Organization: District of Columbia
EdScape

EdScape Beta, short for “education landscape,” provides information critical to making decisions about whether, where, and what type of public schools, school facilities, and school programs are needed in Washington, DC. This new set of information from the Office of the Deputy Mayor for Education (DME) is intended for policymakers, school leaders, and the public. This information will support data transparency and building a coherent public education system.<div><br /></div>EdScape Beta is organized by individual chapters with interactive Tableau dashboards that allow visitors to dig into the information and analyze it by grade band (elementary, middle, high, or adult/alternative), area of the city (ward or neighborhood cluster), or by sector (DCPS or public charter). Each of the dashboards also has downloadable Excel data file that can be used with other software. Each chapter also includes a PDF document that summarizes the information for ease of use.

Tags: dcps, dme, edscape, education, enrollment, osse, public-schools, school, school-population, students, washington-dc
Organization: District of Columbia
Economic Intelligence Dashboard

<p>DMPED is using economic data to drive positive change and build good government for District of Columbia residents. They are focusing on collecting and compiling information about the city, in particular on D.C.’s economic development priorities that create more pathways to the middle class: jobs, quality affordable housing, and community-focused development.</p><p>This site is an online version of the Deputy Mayor for Planning and Economic Development’s weekly dashboard. This dashboard is also transmitted to the City Administrator, the Mayor, and other senior staff, so they can be aware of economic trends and context. It includes only data that is public, so certain indicators that DMPED uses are not included.</p>

Tags: affordable, business, development, district-of-columbia, dmped, economic, employment, housing, inclusive-prosperity, income, real-estate, wages, washington-dc
Organization: District of Columbia