2007 Land Use Land Cover

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This metadata record describes the creation of a 2007 update of the 2002 Land Use and Land Cover data set for the State of Delaware. The land use update was based on aerial imagery collected in the summer of 2007. The imagery was collected with 4 bands: B, G, R, and NIR. The update was performed using statistical differencing techniques to identify changed areas. Areas of change were photointerpreted by an analyst. The work was performed by the Sanborn Map Company, Inc. in Ann Arbor, Michigan.</SPAN></P><P><SPAN>The 2002 land use data were based on the 1997 land-use data of the State and 2002 false color infrared digital orthophotography at a scale of 1:2400.</SPAN></P></DIV></DIV></DIV>

NOAA - Customer satisfaction with NWS services, as measured by the American Customer Satisfaction Index

Weather information users are surveyed continuously by means of a web-based, pop-up survey on NWS web pages throughout the Nation. A sample size of approximately 6,000 responses is collected quarterly for a maximum of 24,000 annual responses. The Customer Satisfaction Index (CSI) score is calculated as a weighted average of three survey questions that measure different facets of satisfaction with NWS services. American Customer Satisfaction Index (ACSI) researchers use proprietary software technology to estimate the weighting. The three questions include the overall satisfaction of NWS services, expectations of service, and a comparison to an ideal organization. Indexes are reported on a 0 to 100 scale. The CSI was started in the United States in 1994 by researchers at the University of Michigan, in conjunction with the American Society for Quality in Milwaukee, Wisconsin, and CFI Group in Ann Arbor, Michigan. The Index was developed to provide information on satisfaction with the quality of products and services available to consumers. The survey data serve as inputs to an econometric model that benchmarks customer satisfaction with more than 300 companies in 43 industries and 10 economic sectors, as well as various services of federal and local government agencies.

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)

Grand Region Watershed Areas

This is the watershed areas for the greater grand region of south west Michigan.

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>

Environmental Sensitivity Project (2015)

Historically, the City of Edmonton has managed ‘natural areas’ within the North Saskatchewan River Valley and the Tablelands separately, guided by inventories such as the Ribbon of Green and Geowest (1993). Over the past decade, City policy has shifted to manage natural areas with consideration of their role within an ecological network. Today, a goal of the City is to protect, preserve and enhance a functioning ecological network throughout the city limits. This network should include lands in both the river valley and the Tablelands. To further this goal, a model was developed in 2015 for determining environmental sensitivity scores across the entirety of the city. This model guided the collection of several digital data layers with coverage across the entire study area (including several ecological assets, threats to assets, and development and cultural constraints). Data layers were then used to develop spatial outputs that summarized the distribution of these assets, threats and constraints. These base layers have been compiled into this dataset to help inform planning, development and conservation throughout Edmonton. Environmental sensitivity analysis incorporated recent mapping of the ecological network of native and non-native vegetation, streams, wetlands and other waterbodies as much as possible, with practical limitations. The City’s urban Primary Land and Vegetation Inventory (uPLVI) and remote sensing data used for this assessment were completed in 2015 and 2013 respectively, which is relatively recent, but not current. Similarly, infrastructure data (roads, subdivision development and stormwater facilities) provided varied in month of acquisition from 2015. Some discrepancy between mapped and actual features may result, due to loss and changes from ongoing development activities.

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).

Resident Satisfaction Survey Results 2018