The Office of Contracting and Procurement (OCP) is required to post contract awards valued at $100,000 or more for agencies served by OCP. The awarded contracts database includes a caption describing the type of goods or services provided, the contract number, the ordering agency, the contract amount, the period of time covered by the contract award, the contractor receiving the award, and the market type. To obtain a copy of any contract, you may submit a FOIA request online via the DC government Public FOIA Portal. Data has been updated to include agency budget code, name, and acronym attributes. Budget codes were used to assign the agency name and acronym to each record. Agencies that share the same budget code, such as those under the Executive Office of the Mayor, were left blank in PASS records. For questions regarding details within the data, contact the Office of Contracting and Procurement at https://contracts.ocp.dc.gov/contact.

The purpose of this site is to allow public access to the City's procurement contracts. These contracts represent a diverse list of resources required by Tempe to support the community's needs including equipment, vehicles, products, materials and services. Click on Open or double arrow to enter full screen mode.In line with the City's strong commitment to transparency and Smart City initiatives, we are pleased to make this information available in this accessible format.A user guide may be viewed by clicking here. Additional InformationSource: The original data source originates from the City of Tempe's Purchasing Contracts document storage and the purchasing contract financials application.Contact (author): Contact E-Mail (author): Contact (maintainer): Michael Greene, Procurement AdministrationContact E-Mail (maintainer): michael_greene@tempe.govData Source Type: Data Source Types are: Sql Server, Oracle and pdf file storage.Preparation Method: Data is extracted using a programmatic automation process that pulls data from the defined sources at regular time intervals.Publish Frequency: Once per week.Publish Method: Automatic via developed automation process.Data Dictionary

A list of County Contracts for 2015.

Tempe is among Arizona's most educated cities, lending to a creative, smart atmosphere. With more than a dozen colleges, trade schools, and universities, about 40 percent of our residents over the age of 25 have Bachelor's degrees or higher. Having such an educated and accessible workforce is a driving factor in attracting and growing jobs for residents in the region.The City of Tempe is a member of the Greater Phoenix Economic Council (GPEC), and with the membership, staff tracks collaborative efforts to recruit business prospects and locations. The Greater Phoenix Economic Council (GPEC) is a performance-driven, public-private partnership. GPEC partners with the City of Tempe, Maricopa County, 22 other communities, and more than 170 private-sector investors to promote the region’s competitive position and attract quality jobs that enable strategic economic growth and provide increased tax revenue for Tempe. This dataset provides the target and actual job creation numbers for the City of Tempe and the Greater Phoenix Economic Council (GPEC). The job creation target for Tempe is calculated by multiplying GPEC's target by twice Tempe's proportion of the population. This page provides data for the New Jobs Created performance measure.The performance measure dashboard is available at 5.02 New Jobs Created. Additional Information Source: Extracted from GPEC monthly and annual reports and proprietary excel filesContact: Madalaine McConvilleContact Phone: 480-350-2927Data Source Type: Excel filesPreparation Method: Extracted from GPEC monthly and annual reports and proprietary Excel filesPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

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Contract & Purchasing Services

The Procurement Agreements dataset provides details about contract agreements between the City of Detroit and suppliers who provide materials, equipment and services to the City. Initial and amended contracts and purchase orders associated with the contracts are included in the dataset, In some cases, purchase orders are generated to pay suppliers for work completed under a contract. If available, a link to the contract agreement document in PDF format is provided in the 'Contract Link' field of each record (row) in the dataset. This dataset is updated weekly with data from the Office of Contracting and Procurement (OCP).

A list of County Contracts for 2016.

This dataset is by fiscal year and provides the percentage of the total money spent on goods and services for competitively generated contracts.This page provides data for the Competitively Generated Contract Spend performance measure.The performance measure dashboard is available at 5.06 Competitively Generated Contracts.Data DictionaryAdditional InformationSource:Contact: Michael GreeneContact E-Mail: michael_greene@tempe.govData Source Type: ExcelPreparation Method: ManualPublish Frequency: AnnuallyPublish Method: Manual

The purpose of the county’s legislative program is to pursue new laws and authorities, oppose laws that are believed to have a negative impact on county government and Lake County residents, and pursue funding from the State of Illinois and the federal government.

Contents:FEDCO 2020: Q1-Q2TRANSIT 2020: Q1-Q2Accuracy: The information is extracted from SAP and validated by Supply Services. There should be no issues with accuracy. Service or Departmental names may change.Attributes:PO : Purchase Order NumberDepartment : City Department where contract was awardedDescription: A brief description of the contract awardedProfessional / Consulting Services: A designation is added if the contract awarded was for professional or consulting services. If it is blank the contract is either goods, other services, materials or construction. The designations used are: PE – Professional Services, Specialized Expertise. PO – Professional Services, Business Model Required Outsourcing. PI – Professional Services, Independent Third Party Oversight. PR – Professional Services, Regulatory Requirements. PW – Professional Services, Fluctuations in Workload or Lack of Internal Resources. PP – Professional Services, Proprietary Service or Unique Market Position. CE – Consulting Services, Specialized Expertise.Follow-on / Amendment: A – Amendment. A3 – Amendment, >$50,000 and 50% of original contract. F – Follow-on Contract. E – Extension. E32(2) – Extension as per Section 32(2) of the Purchasing By-law.Amount – Includes taxes, less any rebates (tax rebates or prompt payment discount).Vendor: Vendor Name, City, Province (State / Country)Non-Competitive Rationale: Section 22(1)(a) – Proprietary Rights. Section 22(1)(b) – Abnormal Market Conditions. Section 22(1)(c) – Only one source of supply acceptable and cost effective. Section 22(1)(d) – Absence of competition for technical reasons. Section 22(1)(e) – Security or confidentiality matters. Section 22(1)(f) – Special Circumstances. Section 22(1)(g) – Non-Competitive Follow-on Contract. Section 22(1)(h) – Professional Services < $50,000. Section 22(1)(i) – Utility.Update Frequency: Semi-AnnuallyContact: Corporate Services: Supply

View the diversity of challenges and opportunities across America's counties within different types of rural regions and communities. Get statistics on people, jobs, and agriculture.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data file GIS API Services Interactive map Zip of CSV files For complete information, please visit https://data.gov.

The Contracts and Procurement Transparency Portal was created by the Office of Contracting and Procurement (OCP) as a public clearinghouse for all information related to the District Government’s contracting and procurement efforts.This portal is broken down into 6 modules, they are as follows: Forecast, Solicitations, Contracts, Purchase Orders, Payments and Independent Agencies. Each of these can be individually download via filters in the application and also at opendata.dc.gov.

This project explores the integration of Geographic Information Systems (GIS) and Natural Language Processing (NLP) to improve job–candidate matching in recruitment. Traditional AI-based e-recruitment systems often ignore geographic constraints. Our hybrid model addresses this gap by incorporating both textual similarity and spatial relevance in matching candidates to job postings.Data UsedCandidate Data (CVs)Source: Scraped from emploi.maSize: 1000 CVs after cleaningContent: Candidate names (anonymized), skills, experiences, locations (coordinates), availability, etc.Job DescriptionsSource: Publicly available dataset from KaggleSize: we took 1000 job postings using category :MoroccoContent: Titles, descriptions, required skills, sector labels, and office locations...All datasets have been cleaned and anonymized for privacy and research ethics compliance.

This map shows the geography and density of employment in Ferndale.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for geographic information science (gis) in the U.S.

This list represents the contracts that the Office of the City Clerk currently has on file. These contracts may be expired or no longer in effect. If you wish to view a contract please make a public records request and include the contract number. Requests can be made at: https://cityofsacramentoca.nextrequest.com/requests/new

Date Created: December 10, 2024Update Frequency: AnnuallyAccuracy, Completeness, and Known Issues: The information is extracted from SAP and validated by Supply Services. There should be no issues with accuracy. Service or Departmental names may change.Attributes:Contract : Contract file numberDepartment : City Department where contract was awardedDescription: A brief description of the contract awardedProfessional / Consulting Services: A designation is added if the contract awarded was for professional or consulting services. If it is blank the contract is either goods, other services, materials or construction. The designations used are: PE – Professional Services, Specialized Expertise. PO – Professional Services, Business Model Required Outsourcing. PI – Professional Services, Independent Third Party Oversight. PR – Professional Services, Regulatory Requirements. PW – Professional Services, Fluctuations in Workload or Lack of Internal Resources. PP – Professional Services, Proprietary Service or Unique Market Position. CE – Consulting Services, Specialized Expertise.Contract Approval Request Type: Contract stageAmount: Excluding taxes, less any rebatesVendor: Vendor Name(s)Non-Competitive Rationale: Section 22(1)(a) – Proprietary Rights. Section 22(1)(b) – Abnormal Market Conditions. Section 22(1)(c) – Only one source of supply acceptable and cost effective. Section 22(1)(d) – Absence of competition for technical reasons. Section 22(1)(e) – Security or confidentiality matters. Section 22(1)(f) – Special Circumstances. Section 22(1)(g) – Non-Competitive Follow-on Contract. Section 22(1)(h) – Professional Services < $50,000. Section 22(1)(i) – Utility. Section 22 (1)(j) additional deliveries by original supplier for technical or financial reasons. Section 22(1)(k) social enterprise supplier Data Steward: Shalane DunlopData Steward Email: fcsdposting@ottawa.caDepartment or Agency: Finance and Corporate Services DepartmentBranch/Unit: Supply Services

Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

Wasatch Front Real Estate Market Model (REMM) Projections

WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

Demographic data from the decennial census
County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
Current employment locational patterns derived from the Utah Department of Workforce Services
Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
Current land use and valuation GIS-based parcel data stewarded by County Assessors
Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level

‘Traffic Analysis Zone’ Projections

The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

‘City Area’ Projections

The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

Summary Variables in the Datasets

Annual projection counts are available for the following variables (please read Key Exclusions note below):

Demographics

Household Population Count (excludes persons living in group quarters) 
Household Count (excludes group quarters) 

Employment

Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
Retail Job Count (retail, food service, hotels, etc)
Office Job Count (office, health care, government, education, etc)
Industrial Job Count (manufacturing, wholesale, transport, etc)
Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).

• These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

Key Exclusions from TAZ and ‘City Area’ Projections

As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Statewide Projections

Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.

The data layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) to show change in job characteristics over time, including total number of jobs, worker age, sectors and earnings, from 2010-2019, by various geographies for the state of Georgia.Data manifest: https://opendata.atlantaregional.com/datasets/employment-and-job-flows-2010-2019-manifest/explore