Description: The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are from the 2011-2015 American Community Survey (ACS). To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/Data Dictionary: DD_Low to Moderate Income Populations by Block GroupDate of Coverage: ACS 2011-2015
Copyright Text: U.S. Department of Housing and Urban Development, U.S. Census Bureau
Description: Licensed Hospital Locations in Kentucky. Locations are placed at the front door or entrance to each facility.
Copyright Text: Hospital Front Door locations provided by the CHFS State Health Operations Center; attributes provided by CHFS Office of Inspector General.
Description: High resolution land cover dataset for Louisville, KY (vector format produced from 1.5 foot raster land cover). Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2019 LiDAR data and 2018 NAIP imagery. Ancillary data sources included GIS data provided by Louisville, KY or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:1000 and all observable errors were corrected.
Copyright Text: University of Vermont Spatial Analysis Laboratory in collaboration with TreesLouisville,the United States Environmental Protection Agency, Louisville and Jefferson County Information Consortium (LOJIC), and Louisville Metro Government.