Household formation by scenario, local authority and year, for the 4 scenarios described in the project methodology for the years 2017-2040 https://www.esri.ie/publications/regional-demographics-and-structural-housing-demand-at-a-county-level The 4 scenarios are: Baseline/Business as usual - based on medium term projections for the economy with an underlying assumption that net inwards migration would converge to 15,000 p.a. by 2024 and remain at that level throughout the projection horizon. 50:50 City - based on a similar outlook in terms of net inwards migration but whereby population growth is distributed in line with the objectives of the National Planning Framework (See National Policy Objectives 1a and 2a of https://npf.ie/wp-content/uploads/Project-Ireland-2040-NPF.pdf) High Migration - assumes that net inwards migration stays at an elevated level throughout the projection horizon (net inwards migration of 30,000 p.a) Low Migration - assumes that net inwards migration falls to net inwards migration of 5,000 by 2022 before converging back to the business as usual levels (i.e. net inwards migration of 15,000 p.a.) by 2027 and remaining at that level thereafter.

After nearly 15 years, I'm finally retiring my personal WordPress blog. I'm now in the process of migrating a few of my favorite posts to this shiny new site. Props to WP, it has served me well. But over the last couple of years, my site has gotten dusty and hard to maintain. In the meantime, the depth and breadth of ArcGIS capabilities are knocking my socks off. This is a new adventure, so thanks for joining me as I start the journey.Full disclosure – I work at Esri (makers of ArcGIS). This is my personal site. My opinions are my own.

ESRI Population Projections by Local Authority. Published by Department of Housing, Local Government, and Heritage. Available under the license Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0).Population projection by scenario, year of age and local authority, for the 4 scenarios described in the project methodology for years 2017-2040. https://www.esri.ie/publications/regional-demographics-and-structural-housing-demand-at-a-county-level

The 4 scenarios are:

Baseline/Business as usual – based on medium term projections for the economy with an underlying assumption that net inwards migration would converge to 15,000 p.a. by 2024 and remain at that level throughout the projection horizon.

50:50 City – based on a similar outlook in terms of net inwards migration but whereby population growth is distributed in line with the objectives of the National Planning Framework (See National Policy Objectives 1a and 2a of https://npf.ie/wp-content/uploads/Project-Ireland-2040-NPF.pdf)

High Migration – assumes that net inwards migration stays at an elevated level throughout the projection horizon (net inwards migration of 30,000 p.a)

Low Migration - assumes that net inwards migration falls to net inwards migration of 5,000 by 2022 before converging back to the business as usual levels (i.e. net inwards migration of 15,000 p.a.) by 2027 and remaining at that level thereafter....

Analysis of ‘ESRI Population Projections by Local Authority’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-data-usmart-io-org-ae1d5c14-c392-4c3f-9705-537427eeb413-dataset-viewdiscovery-datasetguid-87d49471-33e5-4ec8-8cde-69ed29de6da2 on 12 January 2022.

--- Dataset description provided by original source is as follows ---

Population projection by scenario, year of age and local authority, for the 4 scenarios described in the project methodology for years 2017-2040. https://www.esri.ie/publications/regional-demographics-and-structural-housing-demand-at-a-county-level

The 4 scenarios are:

Baseline/Business as usual – based on medium term projections for the economy with an underlying assumption that net inwards migration would converge to 15,000 p.a. by 2024 and remain at that level throughout the projection horizon.

50:50 City – based on a similar outlook in terms of net inwards migration but whereby population growth is distributed in line with the objectives of the National Planning Framework (See National Policy Objectives 1a and 2a of https://npf.ie/wp-content/uploads/Project-Ireland-2040-NPF.pdf)

High Migration – assumes that net inwards migration stays at an elevated level throughout the projection horizon (net inwards migration of 30,000 p.a)

Low Migration - assumes that net inwards migration falls to net inwards migration of 5,000 by 2022 before converging back to the business as usual levels (i.e. net inwards migration of 15,000 p.a.) by 2027 and remaining at that level thereafter.

--- Original source retains full ownership of the source dataset ---

This layer contains the geology derived from the 1958 map prepared by W.P. Woodring, US Geological Survey's geologist, appeared on the Geology of Barro Colorado Island, Canal Zone hard paper copy, published by the Smithsonian Miscellaneous Collections, page 12.

This dataset was updated April, 2024.This ownership dataset was generated primarily from CPAD data, which already tracks the majority of ownership information in California. CPAD is utilized without any snapping or clipping to FRA/SRA/LRA. CPAD has some important data gaps, so additional data sources are used to supplement the CPAD data. Currently this includes the most currently available data from BIA, DOD, and FWS. Additional sources may be added in subsequent versions. Decision rules were developed to identify priority layers in areas of overlap.Starting in 2022, the ownership dataset was compiled using a new methodology. Previous versions attempted to match federal ownership boundaries to the FRA footprint, and used a manual process for checking and tracking Federal ownership changes within the FRA, with CPAD ownership information only being used for SRA and LRA lands. The manual portion of that process was proving difficult to maintain, and the new method (described below) was developed in order to decrease the manual workload, and increase accountability by using an automated process by which any final ownership designation could be traced back to a specific dataset.The current process for compiling the data sources includes: Clipping input datasets to the California boundary Filtering the FWS data on the Primary Interest field to exclude lands that are managed by but not owned by FWS (ex: Leases, Easements, etc) Supplementing the BIA Pacific Region Surface Trust lands data with the Western Region portion of the LAR dataset which extends into California. Filtering the BIA data on the Trust Status field to exclude areas that represent mineral rights only. Filtering the CPAD data on the Ownership Level field to exclude areas that are Privately owned (ex: HOAs) In the case of overlap, sources were prioritized as follows: FWS > BIA > CPAD > DOD As an exception to the above, DOD lands on FRA which overlapped with CPAD lands that were incorrectly coded as non-Federal were treated as an override, such that the DOD designation could win out over CPAD.In addition to this ownership dataset, a supplemental _source dataset is available which designates the source that was used to determine the ownership in this dataset.Data Sources: GreenInfo Network's California Protected Areas Database (CPAD2023a). https://www.calands.org/cpad/; https://www.calands.org/wp-content/uploads/2023/06/CPAD-2023a-Database-Manual.pdf US Fish and Wildlife Service FWSInterest dataset (updated December, 2023). https://gis-fws.opendata.arcgis.com/datasets/9c49bd03b8dc4b9188a8c84062792cff_0/explore Department of Defense Military Bases dataset (updated September 2023) https://catalog.data.gov/dataset/military-bases Bureau of Indian Affairs, Pacific Region, Surface Trust and Pacific Region Office (PRO) land boundaries data (2023) via John Mosley John.Mosley@bia.gov Bureau of Indian Affairs, Land Area Representations (LAR) and BIA Regions datasets (updated Oct 2019) https://biamaps.doi.gov/bogs/datadownload.htmlData Gaps & Changes:Known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. Additionally, any feedback received about missing or inaccurate data can be taken back to the appropriate source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.24_1: Input datasets this year included numerous changes since the previous version, particularly the CPAD and DOD inputs. Of particular note was the re-addition of Camp Pendleton to the DOD input dataset, which is reflected in this version of the ownership dataset. We were unable to obtain an updated input for tribral data, so the previous inputs was used for this version.23_1: A few discrepancies were discovered between data changes that occurred in CPAD when compared with parcel data. These issues will be taken to CPAD for clarification for future updates, but for ownership23_1 it reflects the data as it was coded in CPAD at the time. In addition, there was a change in the DOD input data between last year and this year, with the removal of Camp Pendleton. An inquiry was sent for clarification on this change, but for ownership23_1 it reflects the data per the DOD input dataset.22_1 : represents an initial version of ownership with a new methodology which was developed under a short timeframe. A comparison with previous versions of ownership highlighted the some data gaps with the current version. Some of these known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. In addition, any topological errors (like overlaps or gaps) that exist in the input datasets may thus carry over to the ownership dataset. Ideally, any feedback received about missing or inaccurate data can be taken back to the relevant source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.

The 4 scenarios are: Baseline/Business as usual – based on medium term projections for the economy with an underlying assumption that net inwards migration would converge to 15,000 p.a. by 2024 and remain at that level throughout the projection horizon. 50:50 City – based on a similar outlook in terms of net inwards migration but whereby population growth is distributed in line with the objectives of the National Planning Framework (See National Policy Objectives 1a and 2a of https://npf.ie/wp-content/uploads/Project-Ireland-2040-NPF.pdf) High Migration – assumes that net inwards migration stays at an elevated level throughout the projection horizon (net inwards migration of 30,000 p.a) Low Migration - assumes that net inwards migration falls to net inwards migration of 5,000 by 2022 before converging back to the business as usual levels (i.e. net inwards migration of 15,000 p.a.) by 2027 and remaining at that level thereafter.

The Central Valley Flood Protection Plan (CVFPP) recommends that the California Department of Water Resources (DWR) develop a system for tracking performance of the flood system, including the following actions:• Track the outcomes from flood investments to demonstrate value.• Monitor and track outcomes of multi-benefit projects over time.• Create a tracking system of operations and maintenance investments and outcomes to demonstrate the value that Local Maintaining Agencies attain for their investments.• Track and report changes in the hydrologic and sea level rise conditions and subsidence over time through updates to the Flood System Status Report (FSSR)These recommendations stem from progressive work during the development of the 2012 CVFPP and subsequent 2017 CVFPP update. The DWR Flood Performance Tracking System tracks the CVFPP outcomes related to: (1) improving flood risk management and (2) enhancing ecosystem vitality. This tracking system has the ability to track the status, trends, and changes over time of the ecosystem (including the Conservation Strategy’s Measurable Objectives [CSMOs] as of 2016) outlined in the Conservation Strategy document here: https://cawaterlibrary.net/wp-content/uploads/2017/10/ConservStrat-Nov2016.pdf along with the Flood System metrics outlined in the Flood System Status Report here: https://water.ca.gov/Programs/Flood-Management/Flood-Planning-and-Studies/Central-Valley-Flood-Protection-Plan.The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.1, dated September 11, 2019.This data set was not produced by DWR. Data were originally developed and supplied by ESA, under contract to California Department of Water Resources. DWR makes no warranties or guarantees — either expressed or implied — as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data.Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov.

The Kirwan Institute for the Study of Race and Ethnicity at Ohio State University developed the Detroit Regional Opportunity Index to compare levels of opportunity for people growing up in different parts of a region. The Index was developed by combining many different data indicators for opportunity into a single score. More information on the Detroit methodology and composite data can be found here: http://kirwaninstitute.osu.edu/wp-content/uploads/2014/08/20131211neighborhood.pdf

The full report from Kirwan on the Detroit Opportunity project can be found here: http://kirwaninstitute.osu.edu/?my-product=opportunity-for-all-inequity-linked-fate-and-social-justice-in-detroit-and-michigan/

GIS files showing the boundary of the Old Oak and Park Royal Development Corporation.

Data is available in either ESRI shapefile or MapInfo TAB format.

https://files.datapress.com/london/wp-uploads/20160728114955/OPDC-boundary-01_0.png" alt="OPDC boundary - 01_0">

For details on changes and reasons: https://redistricting.lacounty.gov/wp-content/uploads/2021/11/OP065_v2.pdf - OP 065 - Modified Option F (Ad Hoc Working Group)

Location and details of WCC tracks within the Wellington City area.

Note that this data is updated regularly.

GIS datoteke koje prikazuju granicu Old Oak and Park Royal Development Corporation.

Podaci su dostupni u formatu ESRI shapefile ili MapInfo TAB.

https://files.datapress.com/london/wp-uploads/20160728114955/OPDC-boundary-01_0.png" alt="OPDC granica – 01_0"> GIS datoteke koje prikazuju granicu Old Oak and Park Royal Development Corporation.

Podaci su dostupni u formatu ESRI shapefile ili MapInfo TAB.

https://files.datapress.com/london/wp-uploads/20160728114955/OPDC-boundary-01_0.png" alt="OPDC granica – 01_0">

Fichiers SIG montrant les limites de la Old Oak and Park Royal Development Corporation.

Les données sont disponibles au format ESRI shapefile ou MapInfo TAB.

https://files.datapress.com/london/wp-uploads/20160728114955/OPDC-boundary-01_0.png" alt="OPDC border - 01_0">

View synthesized input from public hearing no. 3 with notations on changes made in ad-hoc working groups: https://redistricting.lacounty.gov/wp-content/uploads/2021/12/CRC-Public-Input-211203-Gayla-WG-compilation.pdf - OP 083 - Modified Option B-2 (Ad Hoc Working Group)

GIS datoteke, ki prikazujejo mejo Old Oak in Park Royal Development Corporation.

Podatki so na voljo v formatu ESRI shapefile ali MapInfo TAB.

https://files.datapress.com/london/wp-uploads/20160728114955/OPDC-boundary-01_0.png" alt="meja OPDC – 01_0">

View synthesized input from public hearing no. 3 with notations on changes made in ad-hoc working groups: https://redistricting.lacounty.gov/wp-content/uploads/2021/12/CRC-Public-Input-211203-Gayla-WG-compilation.pdf - OP 084 - Modified Option G (Ad Hoc Working Group)

REQUIRED: A brief narrative summary of the data set.

What is the Community Need Index?

The Allegheny County Department of Human Services (DHS) conducts a Community Need Index (CNI) to identify specific areas that are in greater need, and face larger socioeconomic barriers, relative to others. The newest version of the CNI index ranks neighborhoods by need level by looking at:

The percentage of families who live below the poverty lineThe percentage of unemployed or unattached malesThe percentage of those aged 25 and up without at least a Bachelor’s degreeThe percentage of single parent householdsThe percentage of households without internet accessRate of homicide per 100,000 residentsRate of fatal overdoses per 100,000 residents

The researchers used a census tract level to break up the region and assess needs. Census tracts are static, relatively small subdivisions of a county.(See https://www.alleghenycountyanalytics.us/2024/05/31/allegheny-county-community-need-index/ for more information.)If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (https://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (https://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.

Category: Human & Social Services

Organization: Allegheny County

Department: Department of Human Services

Temporal Coverage: 2022

Data Notes:

Coordinate System: GCS_North_American_1983

Development Notes: see https://www.alleghenycountyanalytics.us/2024/05/31/allegheny-county-community-need-index/

Related Document(s): 2024 report: https://www.alleghenycountyanalytics.us/wp-content/uploads/2021/05/24-ACDHS-05-CNI-Report_v2.pdf

Frequency - Data Change: as needed

Frequency - Publishing: as needed

Data Steward Name: Nicholas Cotter

Data Steward Email: DHS-Research@alleghenycounty.us

Coastal Ocean Mammal and Bird Education and Research Surveys – BeachCOMBERS

The Coastal Ocean Mammal and Bird Education and Research Surveys (BeachCOMBERS) program was created in 1997 with the objective to train citizen scientists to collect standardized scientific data within Monterey Bay National Marine Sanctuary (MBNMS). Since then, this citizen science program has greatly expanded: we have trained and coordinated more than 150 volunteers to monitor human and natural impacts to coastal wildlife by documenting the deposition of marine birds, mammals, and sea turtles from as far north as Santa Cruz County to as far south as Los Angeles County.

Program objectives are as follows: 1) obtain baseline information on rates of beach deposition of marine birds and mammals; 2) assess causes of seabird and marine mammal mortality; 3) assist resource management agencies in early detection of unusual rates of natural and anthropogenic mortality; 4) assess abundance of tar balls (oil patches) on beaches; 5) build a network of interacting citizens, scientists, and resource managers; and 6) disseminate related information to resources agencies, the public, and educational institutions.

BeachCOMBERS is a collaborative program that has successfully informed resource managers about wildlife impacts from anthropogenic and natural sources such as oil spills, starvation, fishery interactions, harmful algal blooms, plastic ingestion, and entanglements (e.g., Nevins and Harvey 2004, Jessup et al. 2009, Nevins et al. 2011, Donnelly-Greenan et al. 2014, Henkel et al. 2014, Donnelly et al. in prep). The program is a collaboration between Moss Landing Marine Laboratories (MLML), MBNMS, and other state and research institutions including the California Department of Fish and Wildlife (CDFW), US Geological Survey (USGS), and US Fish and Wildlife Service (USFWS) with the specific goal of using deposition of beach-cast carcasses as an index of sanctuary health (see Nevins et al. 2011).

https://www.mlml.calstate.edu/beachcombers/wp-content/uploads/sites/35/2019/07/BC-20-yr-report-summary-1.pdf