This dataset contains files created, digitized, or georeferenced by Chris DeRolph for mapping the pre-urban renewal community within the boundaries of the Riverfront-Willow St. and Mountain View urban renewal projects in Knoxville TN. Detailed occupant information for properties within boundaries of these two urban renewal projects was extracted from the 1953 Knoxville City Directory. The year 1953 was chosen as a representative snapshot of the Black community before urban renewal projects were implemented. The first urban renewal project to be approved was the Riverfront-Willow Street project, which was approved in 1954 according to the University of Richmond Renewing Inequality project titled ‘Family Displacements through Urban Renewal, 1950-1966’ (link below in the 'Other shapefiles' section). For ArcGIS Online users, the shapefile and tiff layers are available in AGOL and can be found by clicking the ellipsis next to the layer name and selecting 'Show item details' for the layers in this webmap https://knoxatlas.maps.arcgis.com/apps/webappviewer/index.html?id=43a66c3cfcde4f5f8e7ab13af9bbcebecityDirectory1953 is a folder that contains:JPG images of 1953 City Directory for street segments within the urban renewal project boundaries; images collected at the McClung Historical CollectionTXT files of extracted text from each image that was used to join occupant information from directory to GIS address datashp is a folder that contains the following shapefiles:Residential:Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and property ownersBlack_rented_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and non-owners of the propertyNon_Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as property owners that were not listed as BlackNon_Black_rented_residential_1953.shp: residential entries in the 1953 City Directory not listed as Black or property ownersResidential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposeslastName: occupant's last namelabelShort: combines the Number and lastName fields for map labeling purposesNon-residential:Black_nonResidential_1953.shp: non-residential entries in the 1953 City Directory listed as Black-occupiedNonBlack_nonResidential_1953.shp: non-residential entries in the 1953 City Directory not listed as Black-occupiedNon-residential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationNAICS6: 2022 North American Industry Classification System (NAICS) six-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS6title: NAICS6 title/short descriptionNAICS3: 2022 North American Industry Classification System (NAICS) three-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS3title: NAICS3 title/short descriptionflag: flags whether the occupant is part of the public sector or an NGO; a flag of '0' indicates the occupant is assumed to be a privately-owned businessrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposesOther shapefiles:razedArea_1972.shp: approximate area that appears to have been razed during urban renewal based on visual overlay of usgsImage_grayscale_1956.tif and usgsImage_colorinfrared_1972.tif; digitized by Chris DeRolphroadNetwork_preUrbanRenewal.shp: road network present in urban renewal area before razing occurred; removed attribute indicates whether road was removed or remains today; historically removed roads were digitized by Chris DeRolph; remaining roads sourced from TDOT GIS roads dataTheBottom.shp: the approximate extent of the razed neighborhood known as The Bottom; digitized by Chris DeRolphUrbanRenewalProjects.shp: boundaries of the East Knoxville urban renewal projects, as mapped by the University of Richmond's Digital Scholarship Lab https://dsl.richmond.edu/panorama/renewal/#view=0/0/1&viz=cartogram&city=knoxvilleTN&loc=15/35.9700/-83.9080tiff is a folder that contains the following images:streetMap_1952.tif: relevant section of 1952 map 'Knoxville Tennessee and Surrounding Area'; copyright by J.U.G. Rich and East Tenn Auto Club; drawn by R.G. Austin; full map accessed at McClung Historical Collection, 601 S Gay St, Knoxville, TN 37902; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphnewsSentinelRdMap_1958.tif: urban renewal area map from 1958 Knox News Sentinel article; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphusgsImage_grayscale_1956.tif: May 18, 1956 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/ARA550590030582/usgsImage_colorinfrared_1972.tif: April 18, 1972 color infrared USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR6197002600096/usgsImage_grayscale_1976.tif: November 8, 1976 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR1VDUT00390010/

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

General Mills, Inc. (GIS): Is It a Cereally Good Investment?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The basemap focuses on bathymetry. It also includes inland waters and roads, overlaid on land cover and shaded relief imagery.

There is a newer version of this available: https://lacounty.maps.arcgis.com/home/item.html?id=6745479a2a4a4982bdea8c88e3211499Data downloaded from LAHSA website on 1/30/2024: https://www.lahsa.org/news?article=944-2023-greater-los-angeles-homeless-count-data2023 Greater Los Angeles Point In Time Count data for the Los Angeles Continuum of Care only - does not include cities of Long Beach, Glendale, and Pasadena.Joined to SPA 2022 geographies: https://lacounty.maps.arcgis.com/home/item.html?id=3b337956e643448d8705e5d991855eb0Data used in StoryMap: https://storymaps.arcgis.com/stories/400d7b75f18747c4ae1ad22d662781a3For more information, please contact the Los Angeles County Enterprise GIS Team: egis@isd.lacounty.gov

The ArcGIS Online US Geological Survey (USGS) topographic map collection now contains over 177,000 historical quadrangle maps dating from 1882 to 2006. The USGS Historical Topographic Map Explorer app brings these maps to life through an interface that guides users through the steps for exploring the map collection:

Finding the maps of interest is simple. Users can see a footprint of the map in the map view before they decide to add it to the display, and thumbnails of the maps are shown in pop-ups on the timeline. The timeline also helps users find maps because they can zoom and pan, and maps at select scales can be turned on or off by using the legend boxes to the left of the timeline. Once maps have been added to the display, users can reorder them by dragging them. Users can also download maps as zipped GeoTIFF images. Users can also share the current state of the app through a hyperlink or social media. This ArcWatch article guides you through each of these steps: https://www.esri.com/esri-news/arcwatch/1014/envisioning-the-past.

zip displays ZIP Code areas. Known Errors/Qualifications: Metropolitan boundaries are delineated well. Rural boundaries are extrapolated in some areas. Refer to US Postal Service for latest ZIP Code routes and for exact individual addresses. Zip codes defined by the US Postal Service as "unique", "military", and "post office box" are not included in this layer as they have no defined areas.

10/2013: While this layer is maintained as a feature class in gdbmaint, the coverage format is still required for certain nightly processing. See Steve Whitney. Lineage: Modified 85704 and 85718 zip code polygons as per USPS records requested by Matt from Tucson Water. Modified 85641 based on boundary of Coyote Creek Lots 1-395. Added 85756 zip code based on KOLD on-line news story, 7/1/08. Modified 85718 boundary per anonymous mapguide printout from City, 5/14/09. Modified 85756/85706 boundary using USPS lookup from parcel layer, 5/19/09. Added 85622 based on Green Valley news article and zip+4 lookup, 8/17/09. Removed 85744 based on zip+4 lookup and it was a single business "unique" zip code for IBM. Modified 85629 based on zip+4 notified by Michelene, 4/22/14. Modified boundary of 85742 and 85737 per Irene Swanson 9/2/14. Modified 85653 as per Robin Freiman 9/11/14. Modified 85704 as per Robin Freiman, 10/31/14. Modified 85658 as per Jennifer Patterson (recorders office) 9/10/15. Modified 85641 as per Michelene 4/8/16. Modified 85741 per Robin 1/13/17. Modified 85755 1/27/17 per Robin. 10/25/17 Modified 85341 and 85321 per Robin. Modify 85653 per Marana. 4/19/21 Modified 85629,85756 and 85641 per Nicholas Jordan. Spatial Domain: Pima County Rectified: parcel Maintenance Organization: PC ITD GIS Maintenance Format: GDB Std Export Primary Source Organization: US Postal Service Primary Source Document: http://new.usps.com Primary Source Date: 2000 Primary Source Format: Paper GIS Contact: Ray Brice

MapGuide Layer Name: Zip Code Areas MapGuide Scale Range: 0 - 100000000

Table from the American Community Survey (ACS) S1602 limited English speaking households (households where no one age 14 and over speaks English "very well"). These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.

(UNCLASSIFIED) Liberia’s infrastructure has been in shambles for decades, mostly due to two recent civil wars and regional instability. Vital services including disaster relief and an operating health care system are nonfunctioning, causing natural disasters and epidemics to be even more dangerous. Intense summer rains often lead to flooding, mostly around the capital of Monrovia. Flooding in this region often destroys homes leaving many displaced. In addition, the healthcare system is highly dependent on foreign aid and assistance with Agencies and NGOs providing 90% of health services. Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name AREA_AFF - Location of disaster event TYPE_1 - Primary classification in geodatabase TYPE_2 - Secondary classification in geodatabase START_DT - Start date of disaster event END_DT - Date disaster event ended TL_AFFECT - Total number affected by disaster event FATALITY - Number of deaths caused by disaster event COMMENTS - Additional comments SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThis HGIS dataset was created through information provided by EM-DAT The International Disaster Database and Relief Web. Additional information regarding significant events was sourced through other sources and news articles. The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)EM-DAT. The International Disaster Database, “Advanced Search”. Last modified September 2014. Accessed September 2014. http://www.emdat.be.ReliefWeb, “Liberia”. Last modified May 2014. Accessed September 2014. http://reliefweb.int.Sources (Metadata)IRIN Humanitarian News and Analysis, “Liberia: Floods displace hundreds in Monrovia.” Last modified August 2007. Accessed September 2014. http://www.irinnews.org.World Health Organization, “Liberia: Health Sector Needs Assessment.” Last modified January 2006. Accessed September 2014. http://www.who.int/en