This study sought to examine community policing from a street-level officer's point of view. Active community police officers and sheriff's deputies from law enforcement agencies were interviewed about their opinions, experiences with, and attitudes toward community policing. For the study 90 rank-and-file community policing officers from 30 law enforcement agencies throughout the United States were selected to participate in a 40- to 60-minute telephone interview. The survey was comprised of six sections, providing information on: (1) demographics, including the race, gender, age, job title, highest level of education, and union membership of each respondent, (2) a description of the community policing program and daily tasks, with questions regarding the size of the neighborhood in terms of geography and population, work with citizens and community leaders, patrol methods, activities with youth/juveniles, traditional police duties, and agency and supervisor support of community policing, (3) interaction between community policing and non-community policing officers, (4) hours, safety, and job satisfaction, (5) police training, and (6) perceived effectiveness of community policing.

This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale. When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or do not own a car? How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards. The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer. Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters. The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis. The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels. The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer. Methodology Every census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway. A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in. The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step. Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect. Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point. Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

Introduction

Inspired by Tree Equity Scores (TES) created by American Forests, the provided dataset compares city-maintained tree coverage in Toronto with neighbourhood demographic data. Their work is instrumental for discussing how urban centres manage the impacts of climate change equitably, specifically regarding the prevalence of urban heat islands. This dataset aims to begin discussions similar to TES in Canada, currently lacking local research.

Data Sources

Trees: Collected from Toronto's fantastic open data portal here.

Demographics: 2016 Statistics Canada data for Toronto Neighbourhoods, again provided by Toronto's open data here.

Methods

Tree data and neighbourhood data were both cleaned before merging. Since the tree data is only segregated by ward, QGIS used to place the available coordinates of each tree in its respective neighbourhood using available boundary data. Once each tree was placed in a neighbourhood, all trees need to be summarized to represent the entire neighbourhood. Though this loses a lot of valuable insight and not all trees are created equal, the Count Characteristic represents a tally of all trees in the neighbourhood, Trunk Sums is the aggregate of all diameter breast height (DBH) measures, followed by each measured divided by the area of the neighbourhood. Again, there is no perfect summary of all trees, and our methods are open to improvement.

Limitations

Though the tree data is updated regularly, the neighbourhood demographic data is from the 2016 census, and neighbourhood composition/ tree placements would have shifted between this temporal gap. This dataset will be updated with 2021 census data when the same formats are available. The new data will also shift the scope and need remapping, as now instead of the 2016 140 neighbourhoods, Toronto has been further divided into 158 neighbourhoods for the new census.

Counts and Sums of DBH were also taken to be broadly representative of tree cover, but trees of different sizes and species would not represent the cover they actually provide. The tree data also only includes trees near streets maintained by the city. It does not include privately maintained streets or public parks, furthering our estimations from accurate measures of neighbourhood tree cover. However, it is an interesting measure of publicly funded/actively maintained tree coverage (other than parks), or the tree coverage of common commercial/residential areas.

Using the My Neighborhood tool on Boston.gov, residents can search an address and find city resources closest to them, including City Councilor information, the nearest public library and park, early voting information, and more. This is the underlying dataset for the My Neighborhood tool. It is the result of joining datasets from multiple departments based on address and location information in the Street Address Management (SAM) system that is administered by the City. Each row in this dataset represents an address in the city and the resources associated with it.

These datasets provide aggregated community risk scores for exposure to flooding using the First Street Foundation Flood Model (Version 1.3) at the county and zip code level. county_flood_score and zcta_flood_score provide the overall community risk score. county_flood_category_score and zcta_flood_category_score provide the risk score to specific categories of infrastructure. Each category; critical infrastructure, social infrastructure, residential properties, roads, and commercial properties, is a component of the overall community risk.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The following fields are in the overall risk datasets:

Attribute

Description

county_id

The county FIPS code

count

The count (#) of infrastructure facilities

flood_score

A score of 1, 2, 3, 4, or 5 is shown. Community risk rankings represent risk as Minimal, Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. County level risks are ranked based on how their total depths compare to counties across the country.

The following fields are in the category risk datasets:

Attribute

Description

FIPS

County FIPS code

ZIP_CODE

ZIP code

count

The approximate length of roads (miles) within the geography of aggregation (i.e. ZIP Code, County)

flood_score

A score (Community Risk level) of 0, 1, 2, 3, 4, or 5 is shown. Community risk levels represent risk as Minimal (0), Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. ZIP Code and County level risks are assessed based on how their total depths compare to ZIP Codes and Counties across the country.

risk_direction

A score of 1, -1, or 0 is shown. These note if flood risk is expected to increase (1), decrease (-1), or remain constant (0) over the next 30 years.

infrastructure_category_id

1= critical infrastructure, 4 = social infrastructure , 6 = residential properties, 8 - roads, 9 = commercial properties

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

The property’s Flood Factor as well as data on economic loss.

The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name

Type

Description

fsid

int

First Street ID (FSID) is a unique identifier assigned to each location

long

float

Longitude

lat

float

Latitude

zcta

int

ZIP code tabulation area as provided by the US Census Bureau

blkgrp_fips

int

US Census Block Group FIPS Code

tract_fips

int

US Census Tract FIPS Code

county_fips

int

County FIPS Code

cd_fips

int

Congressional District FIPS Code for the 116th Congress

state_fips

int

State FIPS Code

floodfactor

int

The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)

CS_depth_RP_YY

int

Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00

CS_chance_flood_YY

float

Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00

aal_YY_CS

int

The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low

hist1_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist1_event

string

Short name of the modeled historic event

hist1_year

int

Year the modeled historic event occurred

hist1_depth

int

Depth (in cm) of flooding to the building from this historic event

hist2_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist2_event

string

Short name of the modeled historic event

hist2_year

int

Year the modeled historic event occurred

hist2_depth

int

Depth (in cm) of flooding to the building from this historic event

adapt_id

int

A unique First Street identifier assigned to each adaptation project

adapt_name

string

Name of adaptation project

adapt_rp

int

Return period of flood event structure provides protection for when applicable

adapt_type

string

Specific flood adaptation structure type (can be one of many structures associated with a project)

fema_zone

string

Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders

footprint_flag

int

Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

This dataset provides information about the number of properties, residents, and average property values for Community Street cross streets in New Roads, LA.

Disability, Independence and Dependency Situations Survey: (***) Population with disabilities that has some difficulty in getting about on the street, according to the type of difficulty, by Autonomous Community, age and sex. Autonomous Community.

This statistic shows the outcome of a survey done in the Netherlands in 2017 in which respondents were asked about the most frequently discussed topics in the streets among neighbors. As of 2017, roughly ** percent of the Dutch respondents indicated they discuss current topics, like the weather or the news, with their neighbors.

About this dataset

Context

The dataset tabulates the population of St. Joe by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of St. Joe across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 51.54% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the St. Joe is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of St. Joe total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for St. Joe Population by Gender. You can refer the same here

The purpose of the Project on Policing Neighborhoods (POPN) was to provide an in-depth description of how the police and the community interact with each other in a community policing (CP) environment. Research was conducted in Indianapolis, Indiana, in 1996 and in St. Petersburg, Florida, in 1997. Several research methods were employed: systematic observation of patrol officers (Parts 1-4) and patrol supervisors (Parts 5-14), in-person interviews with patrol officers (Part 15) and supervisors (Parts 16-17), and telephone surveys of residents in selected neighborhoods (Part 18). Field researchers accompanied their assigned patrol or supervising officer during all activities and encounters with the public during the shift. Field researchers noted when various activities and encounters with the public occurred during these "ride-alongs," who was involved, and what happened. In the resulting data files coded observation data are provided at the ride level, the activity level (actions that did not involve interactions with citizens), the encounter level (events in which officers interacted with citizens), and the citizen level. In addition to encounters with citizens, supervisors also engaged in encounters with patrol officers. Patrol officers and patrol supervisors in both Indianapolis and St. Petersburg were interviewed one-on-one in a private interviewing room during their regular work shifts. Citizens in the POPN study beats were randomly selected for telephone surveys to determine their views about problems in their neighborhoods and other community issues. Administrative records were used to create site identification data (Part 19) and data on staffing (Part 20). This data collection also includes data compiled from census records, aggregated to the beat level for each site (Part 21). Census data were also used to produce district populations for both sites (Part 22). Citizen data were aggregated to the encounter level to produce counts of various citizen role categories and characteristics and characteristics of the encounter between the patrol officer and citizens in the various encounters (Part 23). Ride-level data (Parts 1, 5, and 10) contain information about characteristics of the ride, including start and end times, officer identification, type of unit, and beat assignment. Activity data (Parts 2, 6, and 11) include type of activity, where and when the activity took place, who was present, and how the officer was notified. Encounter data (Parts 3, 7, and 12) contain descriptive information on encounters similar to the activity data (i.e., location, initiation of encounter). Citizen data (Parts 4, 8, and 13) provide citizen characteristics, citizen behavior, and police behavior toward citizens. Similarly, officer data from the supervisor observations (Parts 9 and 14) include characteristics of the supervising officer and the nature of the interaction between the officers. Both the patrol officer and supervisor interview data (Parts 15-17) include the officers' demographics, training and knowledge, experience, perceptions of their beats and organizational environment, and beliefs about the police role. The patrol officer data also provide the officers' perceptions of their supervisors while the supervisor data describe supervisors' perceptions of their subordinates, as well as their views about their roles, power, and priorities as supervisors. Data from surveyed citizens (Part 18) provide information about their neighborhoods, including years in the neighborhood, distance to various places in the neighborhood, neighborhood problems and effectiveness of police response to those problems, citizen knowledge of, or interactions with, the police, satisfaction with police services, and friends and relatives in the neighborhood. Citizen demographics and geographic and weight variables are also included. Site identification variables (Part 19) include ride and encounter numbers, site beat (site, district, and beat or community policing areas [CPA]), and sector. Staffing variables (Part 20) include district, shift, and staffing levels for various shifts. Census data (Part 21) include neighborhood, index of socioeconomic distress, total population, and total white population. District population variables (Part 22) include district and population of district. The aggregated citizen data (Part 23) provide the ride and encounter numbers, number of citizens in the encounter, counts of citizens by their various roles, and by sex, age, race, wealth, if known by the police, under the influence of alcohol or drugs, physically injured, had a weapon, or assaulted the police, counts by type of encounter, and counts of police and citizen actions during the encounter.

This dataset provides information about the number of properties, residents, and average property values for Community Road cross streets in Bay Shore, NY.

The Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset:Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

About this dataset

Context

The dataset tabulates the population of St. Joseph County by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of St. Joseph County across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 51.04% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the St. Joseph County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of St. Joseph County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for St. Joseph County Population by Gender. You can refer the same here

Art Presence & Property Prices in London

Quantifying the Relationship with Online Data

By [source]

About this dataset

This dataset explores the potential relationship between art presence and property prices in London neighborhoods. We conducted an analysis to investigate this by measuring the proportion of Flickr photographs with the keyword ‘art’ attached. We then compared that data to residential property price gains for each Inner London neighborhood, seeking out any associations or correlations between art presence and housing value. Our findings demonstrate the impact of aesthetics on neighborhoods, illustrating how visual environment influences socio-economic conditions. With this dataset, we aim to show how online platforms can be leveraged for quantitative data collection and analysis which can visualize these relationships so as to better understand our urban settings

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset can be used to investigate the relationship between art presence and property prices in London neighborhoods. The dataset includes three columns – Postcode.District, Rank.Mean.Change, and Proportion.Art.Photos – which provide quantitative analyses of the association between art presence and price gains for London neighborhoods.

To use this dataset, first identify the postcode district for which you wish to access data by referencing a street list or PostCodeSearcher website that outlines postcodes for each neighborhood in London(http://postcodesearcher.com/london). This will allow you to easily find properties within each neighborhood as there are specific postcode districts that demarcate boundaries of particular areas (for example W2 covers Bayswater).

Once you have identified a postcode district of interest, review the ‘Rank.Mean Change’ column to explore how residential property prices have changed relative to other areas in Inner London since 2010-13 using fractions (1 = highest gain; 25 = lowest gain). Focusing on one particular location will also provide an idea about their current pricing level compared with others in order to evaluate whether further investment is worthwhile or not based on its past history of growth rates . It is important to note that higher rank numbers indicate higher price gains while lower rank numbers indicate lower price gains relative with respect from 2010-13 timeframe therefore comparing these values across many neighborhoods gives an indication as what area offers more value growth wise over given time period..

Finally pay attention how much did art contributes as far change in property price goes? To answer this question , review ‘Proportion Art Photos’ column which provides ratio of Flickr photographs associated with keyword 'art' attached within given regions helps identify visual characteristics within different localities.. Comparing proportions across various locations provide detail information regarding how much did share visual aesthetic characterstics impacts change in pricings accross different region.. For example it can give us further understandings if majority photographs are made up of urban landscape , abstracts or simply portrait presences had any role play when we look at relativity gains over past few years? Such comparisons help inform our understanding about potential impact art presence can have on changes stay relatively stable even during volatile market times..

By combining this data with other datasets related to demographics, infrastructure and socioeconomics present within londons different areas we can gain further insight which then allows us making informed decisions when it comes investing particular locations .

Research Ideas

• Use this dataset to develop a predictive analytics model to identify areas in London most likely to experience an increase in residential property prices associated with the presence of art.
• Use this dataset to develop strategies and policies that promote both artistic expression and urban development in Inner London neighborhoods.
• Compare the presence of art across inner London boroughs, as well as against other cities, to gain insight into the socio-economic conditions related to the visual environment of a city and its impact on life quality for citizens

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons.org/publicd...

To date, there has been little research on environmental factors to guide interventions and treatments to improve the health of persons aging with long-term physical disabilities. This project will begin to fill this gap in knowledge by examining the role of characteristics in the social and built environment as they interact with underlying impairments and activity limitations to either hinder or promote the full participation of individuals with physical disabilities in society. The project builds on previous work by linking multiple dimensions of the built and social environment to the health trajectories of individuals in the combined Medicare and Medicaid Data file over a period of 10 years (2007-2016). The project focuses on those neighborhood characteristics hypothesized to be related to healthy aging with physical disability, including the density of recreational centers, public transportation, and neighborhood socioeconomic indicators. Researchers examine indicators of neighborhood safety (based on local crime statistics), since fear of crime may discourage individuals from fully accessing resources in their neighborhood. Based on previous work which showed that snow and ice keep older adults homebound, researchers are also including measures of average temperature and precipitation. Measures of street connectivity tap the connected routes within communities, which may facilitate access to social and physical resources. In addition, socioeconomic disadvantage, racial residential segregation, home foreclosure rates, and low employment opportunities, capture the social environment. All the neighborhood built and social environment data has been made available to the larger research and user community through ICPSR (data sharing core). NaNDA is moving! ICPSR is in the process of curating NaNDA measures and adding them to our data holdings. The current version of most NaNDA data is available as a series in our general archive. For the time being, you can still find some data in the NaNDA repository on openICPSR.

This dataset provides information about the number of properties, residents, and average property values for Community Center Street cross streets in Columbia, LA.

About this dataset

Context

The dataset tabulates the population of St. Joseph County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for St. Joseph County. The dataset can be utilized to understand the population distribution of St. Joseph County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in St. Joseph County. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for St. Joseph County.

Key observations

Largest age group (population): Male # 5-9 years (2,311) | Female # 55-59 years (2,305). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the St. Joseph County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the St. Joseph County is shown in the following column.
• Population (Female): The female population in the St. Joseph County is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in St. Joseph County for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for St. Joseph County Population by Gender. You can refer the same here

This dataset compiles a comprehensive database containing 90,327 street segments in New York City, covering their street design features, streetscape design, Vision Zero treatments, and neighborhood land use. It has two scales-street and street segment group (aggregation of same type of street at neighborhood). This dataset is derived based on all publicly available data, most from NYC Open Data. The detailed methods can be found in the published paper, Pedestrian and Car Occupant Crash Casualties Over a 9-Year Span of Vision Zero in New York City. To use it, please refer to the metadata file for more information and cite our work. A full list of raw data source can be found below:

Motor Vehicle Collisions – NYC Open Data: https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95

Citywide Street Centerline (CSCL) – NYC Open Data: https://data.cityofnewyork.us/City-Government/NYC-Street-Centerline-CSCL-/exjm-f27b

NYC Building Footprints – NYC Open Data: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh

Practical Canopy for New York City: https://zenodo.org/record/6547492

New York City Bike Routes – NYC Open Data: https://data.cityofnewyork.us/Transportation/New-York-City-Bike-Routes/7vsa-caz7

Sidewalk Widths NYC (originally from Sidewalk – NYC Open Data): https://www.sidewalkwidths.nyc/

LION Single Line Street Base Map - The NYC Department of City Planning (DCP): https://www.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page

NYC Planimetric Database Median – NYC Open Data: https://data.cityofnewyork.us/Transportation/NYC-Planimetrics/wt4d-p43d

NYC Vision Zero Open Data (including multiple datasets including all the implementations): https://www.nyc.gov/content/visionzero/pages/open-data

NYS Traffic Data - New York State Department of Transportation Open Data: https://data.ny.gov/Transportation/NYS-Traffic-Data-Viewer/7wmy-q6mb

Smart Location Database - US Environmental Protection Agency: https://www.epa.gov/smartgrowth/smart-location-mapping

Race and ethnicity in area - American Community Survey (ACS): https://www.census.gov/programs-surveys/acs

Recorded parcel geography, only showing active parcels, with date recorded parcel was added, date recorded parcel was dropped/retired, date was added, dropped or altered re map layer. Contains geography created since the inception of the basemap in 1995, with some exceptions. Zoning District columns reflect current zoning.This data has been filtered to only include active parcels. Data pushed to ArcGIS Online on November 10, 2025 at 5:19 AM by SFGIS.Data from: https://data.sfgov.org/d/acdm-wktnDescription of dataset columns:

 mapblklot
 For parcels that are condominium lots and share the same 2D space, this is the lowest value Assessor Parcel Number in the set.


 blklot
 Unique Assessor Parcel Number, combination of the Block Number and the Lot Number


 block_num
 Assessor Block Number


 lot_num
 Assessor Lot Number


 from_address_num
 From Address Number, i.e., the lowest address house number


 to_address_num
 To Address Number, i.e., the highest address house number


 street_name
 Street Name


 street_type
 Street Type, or suffix (e.g., ST, BLVD, etc.)


 odd_even
 Odd or Even Address Number Range


 in_asr_secured_roll
 Indicates the record is in the current Assessor Secured Roll


 pw_recorded_map
 Indicates the record is part of the current Public Works basemap dataset, based on recorded map information.


 zoning_code
 Zoning Code - based on City Planning data


 zoning_district
 Zoning District Name - based on City Planning data


 date_rec_add
 This is the date that the documents related the to addition of the parcel was recorded, as tracked by Public Works.


 date_rec_drop
 This is the date that the documents related the to dropping/retiring of the parcel was recorded, as tracked by Public Works.


 date_map_add
 This is the date that Public Works adding the parcel geography.


 date_map_drop
 This is the date that Public Works dropped the parcel geography.


 date_map_alt
 This is the date that Public Works altered the parcel geography.


 project_id_add
 This is the project identifier for the Public Works project related to the addition of the parcel.


 project_id_drop
 This is the project identifier for the Public Works project related to the dropping/retiring of the parcel.


 project_id_alt
 This is the project identifier for the Public Works project related to the altering of the parcel.


 active



 shape
 The geometry, multipolygon format


 centroid_latitude
 The centroid latitude of the parcel


 centroid_longitude
 The centroid longitude of the parcel


 supdist
 Full name of Supervisorial District.


 supervisor_district
 Supervisor District Number


 supdistpad
 Supervisor District Number with zero padding (01,02,...)


 numbertext
 Supervisor District Number as text (ONE, TWO, ...)


 supname
 Name of current Supervisor of District.


 analysis_neighborhood
 San Francisco Neighborhood for analysis, per DataSF.


 police_district
 The corresponding SFPD district.


 police_company
 The corresponding SFPD district company.


 planning_district
 These are grouping of census tracts. Planning Districts are used in various areas of the Planning process, mainly for analysis and management but are also in some parts of the General Plan.


 planning_district_number
 The number corresponding Planning District field.


 data_as_of
 Timestamp the data was last updated in the source system


 data_loaded_at
 Timestamp the data was loaded to the open data portal

Note: If no description was provided by DataSF, the cell is left blank. See the source data for more information.