Map Gallery for overall maps of Neighborhood Associations and Organizations registered with the City of Bloomington Housing and Neighborhood Development Department (HAND) Related Maps Individual Neighborhood Maps Neighborhood Compliance Zone Maps

Map gallery of overall, individual, and compliance zone neighborhood maps in Bloomington

The Traditional Neighborhood Zoning District (TNZD) requires that a Plan Map be adopted by the legislative body. View detailed metadata.The Plan Map must show the boundary of the various TNZD components: Traditional Neighborhood General (required), Traditional Neighborhood Transition-Center (optional) and Traditional Neighborhood Center (optional). The Plan Map also must designate the location of uses permitted only where mapped. The Plan Map is to be used in conjunction with the TNZD Plan Report to determine the types of land use and design standards applicable to any parcel within the Traditional Neighborhood Zoning District.

In 1934, the Federal Housing Administration created a financial mortgage system that rated mortgage risks for properties based on various criteria but was centered on race and ethnicity. This rating system propagated racial segregation that in many ways persists today.

The FHA Underwriting Handbook incorporated color-coded real estate investment maps that classified neighborhoods based on assumptions about a community, primarily their racial and ethnic composition, and not on the financial ability of the residents to satisfy the obligations of a mortgage loan. These maps, created by the Home Owners Loan Corporation (HOLC) were used to determine where mortgages could or could not be issued.

The neighborhoods were categoriezed into four types:
Type A : Best - newer or areas stil in demand
Type B : Still Desirable - areas expected to remain stable for many years
Type C : Definitely Declining - areas in transition
Type D : Hazardous - older areas considered risky

Neighborhoods shaded red were deemed too hazardous for federally-back loans. These "red-lined" neighborhoods were where most African American residents lived.

Many have argued tha the HOLC maps institutionalized discriminating lending practices which not only perpetuated racial segregation but also led to neighborhood disinvestment. Today, neighborhoods classified as Type C and Type D in 2934 make up the majority of neighborhoods in 2016 that are Areas of Concentrated Poverty where 50% or More are People of Color.

The My Neighborhood - Property application allows users to find property information for Baltimore County. This includes parcels and zoning information. Users have the ability to create a customized, printable map as well as a property information report. Users can search for a property and generate a report by entering in an address or 10-digit tax account id

The main purposes of this online map are 1. to demonstrate the Web-Based Geographic Information System (GIS) in the District of Columbia Office of Tax and Revenue (OTR) Real Property Tax Administration (RPTA), and 2. to share detailed real property data and information to real property owners, the public, and other government entities. The rich map and interactive application include relevant real property valuation contributing map layers, links to original source agencies, and a variety of search, query, and analysis options to meet the needs of a wide user base. The location and links to the original DC Boundary Stones add a fun, historical,and educational component.The Office of the Chief Financial Officer, DC Office of Tax and Revenue (OTR), Real Property Assessment Division values all real property in the District of Columbia. The public interactive online DC Office of Tax and Revenue Real Property Assessment Lot Map Search application accompanies the OTR Tax Payer Service Center and may be used to search for and view all real property, related assessment areas, assessment data, and detailed assessment information.

The Community Plans establish neighborhood-specific goals and implementation strategies to achieve the broad objectives laid out in the City’s General Plan. Together, the 34 Community Plans make up the General Plan’s Land Use Element, which plays an important role in bolstering housing and job opportunities, conserving open space and natural resources, and balancing different neighborhoods’ needs.In addition to the 34 Community Plans, City Planning is the lead agency responsible for preparing the Los Angeles International Airport (LAX) Specific Plan and the Port of Los Angeles's Dual Coastal Plan Zone [30000 - 30601], which guide land use consideration at two of the City's proprietary agencies, namely the airport and port.Each Community Plan consists of a policy document and a land use map. The policy document lays out the community’s goals, policies, and programs, while the land use map identifies where certain uses (such as residential, commercial, and industrial) are permitted. Together, the policy document and land use map inform local zoning decisions. Proposed changes to the City’s zoning are usually initiated through Community Plan Updates.Refresh Rate: Monthly

ABOUT THE COMMUNITY SURVEY DATASETFinal Reports for ETC Institute conducted annual community attitude surveys for the City of Tempe. These survey reports help determine priorities for the community as part of the City's on-going strategic planning process. In many of the survey questions, survey respondents are asked to rate their satisfaction level on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (while some questions follow another scale). The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This data is the weighted data provided by the ETC Institute, which is used in the final published PDF report. PERFORMANCE MEASURESData collected in these surveys applies directly to a number of performance measures for the City of Tempe including the following (as of 2021): 1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Victim Not Reporting Crime to Police1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Quality Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community Survey Additional InformationSource: Community Attitude SurveyContact (author): Wydale HolmesContact E-Mail (author): wydale_holmes@tempe.govContact (maintainer): Wydale HolmesContact E-Mail (maintainer): wydale_holmes@tempe.govData Source Type: Excel tablePreparation Method: Data received from vendorPublish Frequency: AnnualPublish Method: Manual

These data reflect results of a household survey implemented in the summer of 2014. The survey randomly sampled households from 23 neighborhoods (census block groups) across 12 cities and 3 counties. Neighborhoods were purposively selected to represent different configurations of social, built, and natural environmental characteristics using the "iUTAH Urban Typology" (https://www.hydroshare.org/resource/84f00a1d8ae641a8af2d994a74f4ccfb/). Data were collected using a drop-off/pick-up methodology, and produced an overall response rate of over 62% (~2,400 respondents). The questionnaire included detailed questions related to household water use and landscaping behaviors, perceptions of water supply and quality, participation in water based recreation, concerns about water issues, and preferences for a range of local and state water policies.

Here we are making public an anonymized version of the large household survey dataset. To protect the identity of respondents, we have removed a few variables and truncated other variables.

Files included here: englishsurveys and spanishsurveys: These folders contain the survey questionnaires used specific to each neighborhood. Codebook in various formats: Tables (xls and csv files) with a list and definition of questions/variables, which correspond to the columns in the data files, and the encoding of the responses. Dataset in various formats: Tables (csv, xls, sas, sav, dta files) containing numeric responses to each question. Each participant's responses correspond to a row of data. Each question corresponds to a column of data. Interpretation of the coded responses is found in the data codebook. Maps: maps of the neighborhoods surveyed. SummaryReports: Summaries of the results that compare across three counties, summary reports for each county, highlight reports for each city.

Summary reports are also available at http://data.iutahepscor.org/mdf/Data/household_survey/ including an overall report that provides comparisons of how these vary across the three counties where we collected data (Cache, Salt Lake, and Wasatch) as well as summary reports for each county and highlights reports for each city.

This map symbolizes the relative population counts for the City's 12 Data Divisions, aggregating the tract-level estimates from the the Census Bureau's American Community Survey 2021 five-year samples. Please refer to the map's legend for context to the color shading -- darker hues indicate more population.If you click on each Data Division, you can view other Census demographic information about that Data Division in addition to the population count.About the Census Data:The data comes from the U.S. Census Bureau's American Community Survey's 2017-2021 five-year samples. The American Community Survey (ACS) is an ongoing survey conducted by the federal government that provides vital information annually about America and its population. Information from the survey generates data that help determine how more than $675 billion in federal and state funds are distributed each year.For more information about the Census Bureau's ACS data and process of constructing the survey, visit the ACS's About page.About the City's Data Divisions:As a planning analytic tool, an interdepartmental working group divided Rochester into 12 “data divisions.” These divisions are well-defined and static so they are positioned to be used by the City of Rochester for statistical and planning purposes. Census data is tied to these divisions and serves as the basis for analyses over time. As such, the data divisions are designed to follow census boundaries, while also recognizing natural and human-made boundaries, such as the River, rail lines, and highways. Historical neighborhood boundaries, while informative in the division process, did not drive the boundaries. Data divisions are distinct from the numerous neighborhoods in Rochester. Neighborhood boundaries, like quadrant boundaries, police precincts, and legislative districts often change, which makes statistical analysis challenging when looking at data over time. The data division boundaries, however, are intended to remain unchanged. It is hoped that over time, all City data analysts will adopt the data divisions for the purpose of measuring change over time throughout the city.

Note: As of April 16, 2021, this dataset will update daily with a five-day data lag.

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://data.sfgov.org/stories/s/Map-of-Cumulative-Cases/adm5-wq8i#new-cases-map

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by "https://data.sfgov.org/Geographic-Locations-and-Boundaries/Analysis-Neighborhoods/p5b7-5n3h ">analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. Percent positivity indicates how widesprea

Natural neighbourhoods are neighbourhood definitions and boundaries created during a consultation with Edinburgh residents. Natural neighbourhood boundaries were created in 2004 as part of a review of ward boundaries. The city has changed much since then, the population has increased, new neighbourhoods have appeared and demolition has taken place in other areas so the 2014 consultation has updated these boundaries. The boundaries will be used by the Council and its partners to plan services, consultations and inform policy and strategy development.

Neighborhood code reference dataset for the Assessor Historical Secured Property Tax Rolls dataset

This data publication contains LiDAR data (for predictor variables) and plot tree data (for response variables) used in the modeling and mapping of species-level basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho.It also contains the Arc/INFO GRID raster maps for both study areas showing predicted species-level basal area and tree density.Our goal is to use LiDAR data to produce precise maps of basic forest structural attributes to benefit forest managers. We will strive to make maps with accuracies surpassing those of maps made using traditional methods and we believe that the high precision and spatial density of LiDAR data offers opportunities for improvement upon traditional forest management practices.These data are directly associated with the publications "Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data" and Corrigendum to "Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data" shown in the cross-reference section.

Original metadata date was 11/15/2010. Metadata modified on 04/25/2011 to adjust citation to include the addition of a DOI (digital object identifier). Minor metadata updates on 02/20/2013. Minor metadata updates on 12/19/2016.

This map symbolizes the relative percentages of adults living below the poverty level for the City's 12 Data Divisions, aggregating the tract-level estimates from the the Census Bureau's American Community Survey 2018 five-year samples. Please refer to the map's legend for context to the color shading -- darker hues indicate a higher level of adults living below the poverty level.If you click on each Data Division, you can view other Census demographic information about that Data Division in addition to the population count.About the Census Data:The data comes from the U.S. Census Bureau's American Community Survey's 2014-2018 five-year samples. The American Community Survey (ACS) is an ongoing survey conducted by the federal government that provides vital information annually about America and its population. Information from the survey generates data that help determine how more than $675 billion in federal and state funds are distributed each year.For more information about the Census Bureau's ACS data and process of constructing the survey, visit the ACS's About page.About the City's Data Divisions:As a planning analytic tool, an interdepartmental working group divided Rochester into 12 “data divisions.” These divisions are well-defined and static so they are positioned to be used by the City of Rochester for statistical and planning purposes. Census data is tied to these divisions and serves as the basis for analyses over time. As such, the data divisions are designed to follow census boundaries, while also recognizing natural and human-made boundaries, such as the River, rail lines, and highways. Historical neighborhood boundaries, while informative in the division process, did not drive the boundaries. Data divisions are distinct from the numerous neighborhoods in Rochester. Neighborhood boundaries, like quadrant boundaries, police precincts, and legislative districts often change, which makes statistical analysis challenging when looking at data over time. The data division boundaries, however, are intended to remain unchanged. It is hoped that over time, all City data analysts will adopt the data divisions for the purpose of measuring change over time throughout the city.

Table of neighbourhoods used to calculate the stakes of numbers of inhabitants and jobs for each flood scenario.

A series of spatial data produced by the GIS High Flood Risk Land Flood Directive (TRI) of the French Metropolis and mapped for reporting purposes for the European Flood Directive. European Directive 2007/60/EC of 23 October 2007 on the assessment and management of flood risks (OJ L 288, 06-11-2007, p. 27) influences the flood prevention strategy in Europe. It requires the production of flood risk management plans to reduce the negative consequences of flooding on human health, the environment, cultural heritage and economic activity. The objectives and implementation requirements are set out in the Law of 12 July 2010 on the National Commitment for the Environment (LENE) and the Decree of 2 March 2011. In this context, the primary objective of flood and flood risk mapping for IRRs is to contribute, by homogenising and objectivating knowledge of flood exposure, to the development of flood risk management plans (WRMs). This dataset is used to produce flood surface maps and flood risk maps that represent flood hazards and issues at an appropriate scale, respectively. Their objective is to provide quantitative evidence to further assess the vulnerability of a territory for the three levels of probability of flooding (high, medium, low).

Provides grantee information for the third round of Neighborhood Stabilization Program (NSP) formula funding (referred to as NSP3) authorized under Section 1497 of the Wall Street Reform and Consumer Protection Act of 2010.The Neighborhood Stabilization Program (NSP) provides emergency assistance to state and local governments for the acquisition and redevelopment of foreclosed properties that might otherwise become sources of abandonment and blight within their communities.Section 1497 of the Wall Street Reform and Consumer Protection Act of 2010, also known as the Dodd-Frank Act, provided a third round of funding in 2010. NSP3 provides grants to states, local governments, nonprofits and a consortium of nonprofit entities on a competitive basis.Grantee target area data provided through this service was created from user generated areas drawn by grantees using the NSP3 online map tool at available at https://www.huduser.org/NSP/NSP3.html. . To learn more about the Neighborhood Stabilization Program (NSP) visit: https://www.hudexchange.info/programs/nsp/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_NSP3 Grantee Target AreasDate of Coverage: 12/2014

The selected New York City Places point file was created as a guide to New York City’s non-neighborhood place locations that appear in the “New York: A City of Neighborhoods” map poster and webpage. These place locations includes parks, cemeteries, and airports. Best estimates of label centroids were established at a 1:1,000 scale, but are ideally viewed at a 1:50,000 scale.

The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. Much of the ACS data provided on the Census Bureau's Web site are available separately by age group, race, Hispanic origin, and sex. Summary files, Subject tables, Data profiles, and Comparison profiles are available for the nation, all 50 states, the District of Columbia, Puerto Rico, every congressional district, every metropolitan area, and all counties and places with populations of 65,000 or more. Detailed Tables contain the most detailed cross-tabulations published for areas 65k and more. The data are population counts. There are over 31,000 variables in this dataset.

Thoughtful and effective planning enables a city to grow responsibly while providing the community with a variety of opportunities to live, work, and enjoy an environment. Good planning plays a vital role in shaping the future of Long Beach by providing the perfect balance of new development, community preservation, essential services, and economic growth. This map shows two key layers to planning: zoning and land use. It also includes historic districts and parking impacted areas. This map is used in the Zoning and General Plan web mapping application.Zoning Regulations divide the City into districts within which the location, height and bulk of buildings or structures and the uses of buildings, structures or land are regulated as specified. The municipal code defines zoning in Chapter 21.30. The Long Beach General Plan is a policy document that sets forth the goals, policies, and directions the City will take to achieve the vision of the community. The Land Use layer shown in this map is from the Land Use Element established in 1989.The Parking Impacted Area was developed through an extensive parking survey conducted with the help of a consultant to determine residential areas in which at least 75% of the on-street parking spaces were occupied at night.Historic districts are areas containing groups of older houses that are intact and unaltered. While each building may not be individually worthy of landmark status, collectively they preserve the visual qualities and ambiance of the past. Streetscape features, such as trees or light standards, may contribute to the historic value of the district.For more information, please see the Community Development Department website. Map updated 11/2019.