Beach E. coli Predictions

The Chicago Park District issues swim advisories at beaches along Chicago's Lake Michigan lakefront based on E. coli levels. This dataset shows predicted E. coli levels based on an experimental analytical modeling approach.

Beach Lab Data

The Chicago Park District collects and analyzes water samples from beaches along Chicago’s Lake Michigan lakefront. The Chicago Park District partners with the University of Illinois at Chicago Department of Public Health Laboratory to analyze water samples using a new DNA testing method called Rapid Testing Method (qPCR analysis) which tests for Enterococci in order to monitor swimming safety. The rapid testing method (qPCR analysis) is a new method that measures levels of pathogenic DNA in beach water. Unlike the culture based test that requires up to 24 hours of processing, the new rapid testing method requires a 4-5 hours for results. The Chicago Park District can use results of the rapid test to notify the public when levels exceed UPEPA recommended levels, which is 1000* CCE. When DNA bacteria levels exceed 1000 CCE, a yellow swim advisory flag is implemented. For more information please refer to the USEPA Recreational Water Quality Criteria (http://water.epa.gov/scitech/swguidance/standards/criteria/health/recreation). Historically, the Chicago Park District used the culture based analysis method and statistical prediction models to monitor beach water quality. The culture based method tests for Escherichia coli (E. coli) bacteria which is an indicator species for the presence of disease-causing bacteria, viruses, and protozoans that may pose health risks to the public. This method requires 18-24 hours of processing to receive results. The Chicago Park District would use results of the culture based method to notify the public when levels exceed UPEPA recommended levels, which is 235* CFU. When bacteria levels exceed 235 CFU, a yellow swim advisory flag was implemented. This standard is still used at most beaches throughout the Great Lakes region. For more information please refer to the USEPA Recreational Water Quality Criteria. The statistical prediction model forecasted real-time Escherichia coli (E. coli) bacteria levels present in the water. The Chicago Park District (CPD) in partnership with the US Geological Survey, developed statistical prediction models by using weather data pulled from CPD buoys (https://data.cityofchicago.org/d/qmqz-2xku) and weather stations (https://data.cityofchicago.org/d/k7hf-8y75). The Chicago Park District would use results of the predictive model to notify the public when bacteria levels would exceed 235 CFU. When bacteria levels exceed 235 CFU, a yellow swim advisory flag was implemented. * The unit of measurement for Escherichia coli is Colony Forming Units (CFU) per 100 milliliters of water. (Culture Based Method / Statistical Prediction Model) *The unit of measuring DNA is Enterococci Calibrator Cell Equivalents (CCE) per 100 milliliters of water. (Rapid Testing Analysis)

MDOT Plant Manual for Slope Planting

This plant manual identifies plants and planting practices ideal for slope stabilization along urban highways. Appropriate plant selections are adapted to environmental stresses and harsh site conditions along depressed highway slopes found in urban areas. The plant selections also meet additional design criteria (e.g., low growing to allow clear vision, aesthetic appeal).  This research was conducted by Michigan State University Department of Horticulture and Dr. Cregg. This research was funded and managed by the Michigan Department of Transportation, Nanette Alton and Yige Qu - Project Managers. This dataset is intended to be updated annually, as needed by roadside development staff. By using this dataset, you are accepting the terms of use attached. Dataset Owner Contact: MDOT-PlantManual@michigan.gov

Assessor [Archived 05-31-2023] - Parcel Universe

A complete, historic universe of Cook County parcels with attached geographic, governmental, and spatial data. When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded. Additional notes:<ul><li>Data is attached via spatial join (st_contains) to each parcel's centroid.</li> <li>Centroids are based on <a href="https://datacatalog.cookcountyil.gov/Property-Taxation/ccgisdata-Parcel-2021/77tz-riq7">Cook County parcel shapefiles</a>.</li> <li>Older properties may be missing coordinates and thus also missing attached spatial data (usually they are missing a parcel boundary in the shapefile).</li> <li>Newer properties may be missing a mailing or property address, as they need to be assigned one by the postal service.</li> <li>Attached spatial data does NOT go all the way back to 1999. It is only available for more recent years, primarily those after 2012.</li> <li>The universe contains data for the current tax year, which may not be complete or final. PINs can still be added and removed to the universe up until the Board of Review closes appeals.</li> <li>Data will be updated monthly.</li> <li>Rowcount and characteristics for a given year are final once the Assessor <a href="https://www.cookcountyassessor.com/assessment-calendar-and-deadlines">has certified the assessment roll</a> for all townships.</li> <li>Depending on the time of year, some third-party and internal data will be missing for the most recent year. Assessments mailed this year represent values from last year, so this isn't an issue. By the time the Data Department models values for this year, those data will have populated.</li> <li>Current property class codes, their levels of assessment, and descriptions can be found <a href="https://prodassets.cookcountyassessor.com/s3fs-public/form_documents/classcode.pdf">on the Assessor's website</a>. Note that class codes details can change across time.</li> <li>Due to decrepencies between the systems used by the Assessor and Clerk's offices, <i>tax_district_code</i> is not currently up-to-date in this table.</li></ul> For more information on the sourcing of attached data and the preparation of this dataset, see the <a href="https://gitlab.com/ccao-data-science---modeling/data-architecture">Assessor's data architecture repo</a> on GitLab. <a href="https://datacatalog.cookcountyil.gov/stories/s/i22y-9sd2">Read about the Assessor's 2022 Open Data Refresh.</a>

Crustaceans and Demersal, Large Pelagic and Small Pelagic Fish (Operational Data)

Operational data provides operational information of sampled fishing vessels that contains specifications of vessel, engine and fishing gears, and operational inputs. The variables of these data are: organization/project; ID trip; surveyor; landing site/collector; fishing ground (fisheries management area/WPP); sampling time; time; commodity; vessel name/owner; mark; vessel owner; captain name; departure; arrival; origin port; crew (individual); vessel length; vessel weight; main engine; second engine; vessel material; light power; vessel space (unit); box total (unit); space/box capacity; auxiliary vessel; auxiliary vessel; size; freezer; freezer capacity; GPS use ; GPS type; andon (mobile fishing from other provinces) vessel; andon vessel provenance; fishing gear; fishing gear specifications; value; unit; material; tools operated (unit); fishing areas; setting total; ice block total; water depth (start); water depth (real); number of days per trip; number of days per catch; FAD use; FAD visited; FAD succeeded; fishing time; soaking (hour); comment; resources; total catch for consume in volume (Kg); total catch for consume in units (Ind); total catch in volume (Kg); total catch in units (Ind); local name; common name; species name; FAO; haul (Kg); units (Ind); storage method (fresh); storage method (frozen); storage method (salted); storage method (loin); storage method (boiled). The operational data can be used to estimate catch per unit effort (CPUE) of the fisheries and socioeconomics of the fisheries including associated cost.

Agricultural Land Dynamics and Land Policy in Rural Tanzania 2016: Soil/Water Conservation Dataset

This dataset contains information about practices around soil and water conservation. The table can be combined with other datasets in this data asset using the 'hhid' column. The purpose of collecting these data was to examine farm expansion and labor markets in rural Tanzania. Data were collected in 8 rural districts of Tanzania: Mvomero, Kilombero, Njombe, Kiteto, Magu, Moshi Rural, Mkuranga and Liwale. The data were collected through the Feed the Future Innovation Lab for Food Security Policy (FSP).

California Protected Area Database (HESS)

California Protected Area Database (CPAD) for development of the Parcel Inventory dataset for the Housing Element Site Selection (HESS) Pre-Screening Tool. This feature set contains a subset of the statewide source. Its geographic extent covers the San Francisco Bay Region. CPAD inventories open space lands that have been protected for open space uses through fee ownerships. CPAD is not a database of all public lands – for example, it does not include public buildings, water treatment sites, or other non-open space public land. CPAD is suitable for a wide range of planning, assessment, analysis, and display purposes. CPAD should not be used as the basis for official regulatory, legal, or other such governmental actions without more detailed review of current official land records in the area of focus. This feature set contains the CPAD Super Unit features of the database. Super Units are aggregations of Units (which themselves are aggregations of Holdings) to create use-focused polygons for each site name (e.g. Las Trampas Regional Wilderness). Super Units are useful for recreation applications and for cartographic representation. Note: ● Super Units aggregate units based on the managing agency. ● Super Units maintain distinct units for different types of public access. ● Super Units cross county boundaries. ● Super Units have fewer attributes and are primarily used for cartography/display purposes, and to support recreational access applications. The lands in CPAD are defined by their owning and managing agencies at the Holdings and Units levels. At the Super Units level (a version of the release meant primarily for recreation applications, and for general cartography), CPAD lands are defined simply by name, managing agency, and public access. Access to CPAD GIS data is primarily through the State of California’s Atlas open data portal – a download link and more information about CPAD, including a PDF manual about the data, is at https://www.calands.org/cpad/. CPAD is released in shapefile format. The state site also hosts map services with CPAD data displayed by Access Type, Agency Classification, and Agency Level.

Resident Satisfaction Survey Results 2018

USAID Construction Assessment, Subawards: Section 1

This dataset contains data on subwards identified in the survey of USAID construction carried out between June 1, 2011 to June 20 to learn about the character, scope, value and management of USAID supported construction activities. In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully. The USAID construction assessment is a survey of the character, scope, value and management of construction activities supported by USAID during the period from June 1, 2011 to June 20, 2013.