GRU Customer Reclaimed Water Consumption

Monthly reclaimed water consumption in Kilo-gallons (kgals) by service address for all customers in the GRU Service Area. Reclaimed water is also known as sewer or wastewater. (Potable water use can be found in another dataset)

Stormwater_Features

For NPDES Stormwater sewer system enhanced mapping project. Contains a GIS polygon feature class of stormwater basins in Gainesville, FL as a result of the NPDES stormwater system mapping project. This feature does not participate in the GIS network, and is for cartographic purposes only. This file is current only up to 02/04/08 and may be incomplete, and only covers those areas of Gainesville, FL that have been mapped up to 02/04/08. The file is also subject to constant updating as project progresses. This feature class is for informational purposes only. Do not rely on this file for accuracy of dimensions, size or location. The City of Gainesville does not assume responsibility to update this information for any error or omission in this file. This shapefile may indicate the zoning/land use on the properties as shown. Do not rely on this file for accuracy of dimensions. For specific information, contact the City of Gainesville, Florida.

Iowa Geographic Names

This dataset provides the geographic names data for Iowa. All names data products are extracted from the Geographic Names Information System (GNIS), the Federal Government's repository of official geographic names. The GNIS contains the federally recognized name of each feature and defines its location by State, county, USGS topographic map, and geographic coordinates. GNIS also lists variant names, which are non-official names by which a feature is or was known. Other attributes include unique Feature ID and feature class. Feature classes under the purview of the U.S. Board on Geographic Names include natural features, unincorporated populated places, canals, channels, reservoirs, and more.

NOAA - Percentage of the continental U.S. population served by flood inundation mapping services

For more than two decades, the emergency management community has articulated a need for real-time, street-level flood inundation maps depicting the areal extent, depth, and infrastructure impacted by flood waters, to inform critical decisions that save lives and property before, during, and, after a flood event. NOAA will collect and integrate high-resolution bathymetric and topographic data to advance flood and inundation mapping capabilities, particularly for previously underserved communities inland and along the coast. NOAA tracks the progress of making real-time flood inundation mapping services available for 100% of the U.S. population, using Oak Ridge National Laboratory LandScan 2019 data and the latest Census data to assess how many residents lie within the areas served by FIM.

Parcel

South Sudan Unity State Baseline Report: WASH

To get a better understanding and assess the severity of the nutrition and mortality situation in Mayendit County, implementing partners conducted a Nutrition and Mortality SMART survey from the 10th to 23rd of December, 2015. The overall survey objective was to determine the nutrition status among children aged 6 to 59 months and to estimate crude and under-five retrospective mortality rates in Mayendit County, Unity State. Data collected included morbidity data (two-week recall), immunization and supplementation coverage, and a qualitative component on Food Security and Livelihoods (FSL).

RSBS SMO: Part 2 of 2, New York State Residential Statewide Baseline Study: Single and Multifamily Occupant Telephone or Web Survey

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. This is part 2 (contains: Clothes Washing and Drying; Water Heating; Home Lighting; Pool and Spa; Small Household Appliances; and Miscellaneous Equipment) of 2; part 1 (https://data.ny.gov/d/3m6x-h3qa) contains: Behavior and Demographics; Building Shell; Kitchen Appliances; and Heating and Cooling. The New York State Energy Research and Development Authority (NYSERDA), in collaboration with the New York State Department of Public Service (DPS), conducted a statewide residential baseline study (study) from 2011 to 2014 of the single-family and multifamily residential housing segments, including new construction, and a broad range of energy uses and efficiency measures. This dataset includes 2,982 single-family and 379 multifamily occupant survey completes for a total of 3,361 responses. The survey involved 2,285 Web, 1,041 telephone, and 35 mini-inspection surveys. The survey collected information on the following building characteristics: building shell, kitchen appliances, heating and cooling equipment, water heating equipment, clothes washing and drying equipment, lighting, pool and spa equipment, small household appliances, miscellaneous energy consuming equipment, as well as behaviors and characteristics of respondents.

Land Use_data

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe’s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe’s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.

SWGIHubCorShp

Maryland's green infrastructure is a network of undeveloped lands that provide the bulk of the state's natural support system. These data map hub and corridor elements within the green infrastructure. The Green Infrastructure Assessment was developed to provide decision support for Maryland's Department of Natural Resources land conservation programs. Ecosystem services, such as cleaning the air, filtering water, storing and cycling nutrients, conserving soils, regulating climate, and maintaining hydrologic function, are all provided by the existing expanses of forests, wetlands, and other natural lands. These ecologically valuable lands also provide marketable goods and services, like forest products, fish and wildlife, and recreation. The Green Infrastructure serves as vital habitat for wild species and contributes in many ways to the health and quality of life for Maryland residents. To identify and prioritize Maryland's green infrastructure, we developed a tool called the Green Infrastructure Assessment (GIA). The GIA was based on principles of landscape ecology and conservation biology, and provides a consistent approach to evaluating land conservation and restoration efforts in Maryland. It specifically attempts to recognize: a variety of natural resource values (as opposed to a single species of wildlife, for example), how a given place fits into a larger system, the ecological importance of natural open space in rural and developed areas, the importance of coordinating local, state and even interstate planning, and the need for a regional or landscape-level view for wildlife conservation. The GIA identified two types of important resource lands - "hubs" and "corridors." Hubs typically large contiguous areas, separated by major roads and/or human land uses, that contain one or more of the following: Large blocks of contiguous interior forest (containing at least 250 acres, plus a transition zone of 300 feet) Large wetland complexes, with at least 250 acres of unmodified wetlands; Important animal and plant habitats of at least 100 acres, including rare, threatened, and endangered species locations, unique ecological communities, and migratory bird habitats; relatively pristine stream and river segments (which, when considered with adjacent forests and wetlands, are at least 100 acres) that support trout, mussels, and other sensitive aquatic organisms; and existing protected natural resource lands which contain one or more of the above (for example, state parks and forests, National Wildlife Refuges, locally owned reservoir properties, major stream valley parks, and Nature Conservancy preserves). In the GIA model, the above features were identified from Geographic Information Systems (GIS) spatial data that covered the entire state. Developed areas and major roads were excluded, areas less than 100 contiguous acres were dropped, adjacent forest and wetland were added to the remaining hubs, and the edges were smoothed. The average size of all hubs in the state is approximately 2200 acres. Corridors are linear features connecting hubs together to help animals and plant propagules to move between hubs. Corridors were identified using many sets of data, including land cover, roads, streams, slope, flood plains, aquatic resource data, and fish blockages. Generally speaking, corridors connect hubs of similar type (hubs containing forests are connected to one another; while those consisting primarily of wetlands are connected to others containing wetlands). Corridors generally follow the best ecological or "most natural" routes between hubs. Typically these are streams with wide riparian buffers and healthy fish communities. Other good wildlife corridors include ridge lines or forested valleys. Developed areas, major roads, and other unsuitable features were avoided.

Guatemala Maternal health and Sanitation Data: Section 1

Data on maternal Health and household sanitation in Segamil and Paisano in Guatemala 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.