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.

This dataset contains data collected during a study "Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities" (Sustainable Cities and Society (SCS), vol.82, 103906) conducted by Martin Lnenicka (University of Pardubice), Anastasija Nikiforova (University of Tartu), Mariusz Luterek (University of Warsaw), Otmane Azeroual (German Centre for Higher Education Research and Science Studies), Dandison Ukpabi (University of Jyväskylä), Visvaldis Valtenbergs (University of Latvia), Renata Machova (University of Pardubice).

This study inspects smart cities’ data portals and assesses their compliance with transparency requirements for open (government) data by means of the expert assessment of 34 portals representing 22 smart cities, with 36 features.

It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities.

Purpose of the expert assessment The data in this dataset were collected in the result of the applying the developed benchmarking framework for assessing the compliance of open (government) data portals with the principles of transparency-by-design proposed by Lněnička and Nikiforova (2021)* to 34 portals that can be considered to be part of open data ecosystems in smart cities, thereby carrying out their assessment by experts in 36 features context, which allows to rank them and discuss their maturity levels and (4) based on the results of the assessment, defining the components and unique models that form the open data ecosystem in the smart city context.

Methodology Sample selection: the capitals of the Member States of the European Union and countries of the European Economic Area were selected to ensure a more coherent political and legal framework. They were mapped/cross-referenced with their rank in 5 smart city rankings: IESE Cities in Motion Index, Top 50 smart city governments (SCG), IMD smart city index (SCI), global cities index (GCI), and sustainable cities index (SCI). A purposive sampling method and systematic search for portals was then carried out to identify relevant websites for each city using two complementary techniques: browsing and searching. To evaluate the transparency maturity of data ecosystems in smart cities, we have used the transparency-by-design framework (Lněnička & Nikiforova, 2021)*. The benchmarking supposes the collection of quantitative data, which makes this task an acceptability task. A six-point Likert scale was applied for evaluating the portals. Each sub-dimension was supplied with its description to ensure the common understanding, a drop-down list to select the level at which the respondent (dis)agree, and a comment to be provided, which has not been mandatory. This formed a protocol to be fulfilled on every portal. Each sub-dimension/feature was assessed using a six-point Likert scale, where strong agreement is assessed with 6 points, while strong disagreement is represented by 1 point. Each website (portal) was evaluated by experts, where a person is considered to be an expert if a person works with open (government) data and data portals daily, i.e., it is the key part of their job, which can be public officials, researchers, and independent organizations. In other words, compliance with the expert profile according to the International Certification of Digital Literacy (ICDL) and its derivation proposed in Lněnička et al. (2021)* is expected to be met. When all individual protocols were collected, mean values and standard deviations (SD) were calculated, and if statistical contradictions/inconsistencies were found, reassessment took place to ensure individual consistency and interrater reliability among experts’ answers. *Lnenicka, M., & Nikiforova, A. (2021). Transparency-by-design: What is the role of open data portals?. Telematics and Informatics, 61, 101605 *Lněnička, M., Machova, R., Volejníková, J., Linhartová, V., Knezackova, R., & Hub, M. (2021). Enhancing transparency through open government data: the case of data portals and their features and capabilities. Online Information Review.

Test procedure (1) perform an assessment of each dimension using sub-dimensions, mapping out the achievement of each indicator (2) all sub-dimensions in one dimension are aggregated, and then the average value is calculated based on the number of sub-dimensions – the resulting average stands for a dimension value - eight values per portal (3) the average value from all dimensions are calculated and then mapped to the maturity level – this value of each portal is also used to rank the portals.

Description of the data in this data set Sheet#1 "comparison_overall" provides results by portal Sheet#2 "comparison_category" provides results by portal and category Sheet#3 "category_subcategory" provides list of categories and its elements

Format of the file .xls

Licenses or restrictions CC-BY

For more info, see README.txt

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 2018 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 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.

As a mandate of the New Orleans City Data Policy - Executive Order 16-01 & Policy Memorandum 135, we are taking an inventory of all City datasets. This on-going inventory process will help us to categorize and identify data that could be made publicly available. This process also assists our ability to work cross-departmentally and increases our resilience.

Why is the data inventory important? • Stimulate new ideas and services. By publishing a data inventory, city departments may help to stimulate new and innovative ideas from the community. • Increase internal sharing and resilience. A data inventory can also help us access information from other departments that we need to improve service delivery and resilience planning. • Enabling better and more up-to-date processes. The process of publishing a data inventory will help us to realize the constraints of current City technology and processes, and then plan for future improvements. • Changing how we use data. A data inventory can help empower us to change how we use, share and consume our data externally and internally, ultimately transforming data into better services for citizens and fostering continuous improvement.

This city boundary shapefile was extracted from Esri Data and Maps for ArcGIS 2014 - U.S. Populated Place Areas. This shapefile can be joined to 500 Cities city-level Data (GIS Friendly Format) in a geographic information system (GIS) to make city-level maps.

Ethical Data Management

Dudley is a city with a population of 328,356 and lies in the 3000-5000 (High) density category. The city has an area of 106.57 km² with a total green space of 45% and a tree coverage of 25%. The city lies in the North Temperate Zone of the world. The city has improved its Percentage of urban area covered by grass when compared to Global Average and also improved its Average health of urban vegetation when compared to previous year. Within Europe, 9.8% of cities are ranked lower than Dudley. There has been a positive increase in the greenness of city as compared to previous year, out of all the changes happening 52% of them are positive.

Information about accesses (visits) of city data assets. Combines analytics from both employee (citydata.mesaaz.gov) and public data (data.mesaaz.gov) portals.

The following usage types are included in the Access Type column: grid view – tabular view of the dataset / filtered view primer page view – dataset / filtered view’s homepage, includes metadata and table preview of the data download – download of the dataset / filtered view to CSV, JSON, etc. api read access – programmatic access of dataset/filtered vew, etc. story page view – accessing a story page asset visualization page view – accessing a chart or map asset measure page view – accessing a performance measure asset

Usage data are segmented into the following user types: site member: users who have logged in and have been granted a role on the domain community user: users who have logged in but do not have a role on the domain anonymous: users who have not logged in to the domain Data are updated by a system process at least once a day.

Please see Site Analytics: Asset Access for more detail.

World Cities provides a basemap layer for the cities of the world. The cities include national capitals, provincial capitals, major population centers, and landmark cities. Population estimates are provided for those cities listed in open source data from the United Nations Statistics Division, United Nations Human Settlements Programme, and U.S. Census Bureau.

Abstract (en): This data collection is a compendium of data for all counties in the United States for the period 1944 to 1977. The data provide diverse information such as local government activities, population estimates and characteristics, and housing unit descriptors. Also included is information on local government revenues, property taxes, capital outlay, debts, expenditures on education, highways, public welfare, health and hospitals, and police, as well as information on births, deaths, schooling, labor force, employment, family income, family characteristics, electoral votes, and housing characteristics. Additional variables provide information on manufacturing, retail and wholesale trade, banking, mineral industries, farm population, agriculture, crime, and weather. Users may also be interested in the related data collection, COUNTY AND CITY DATA BOOK [UNITED STATES] CONSOLIDATED FILE: CITY DATA, 1944-1977 (ICPSR 7735). ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Checked for undocumented or out-of-range codes.. Individual states, the District of Columbia, and counties or county equivalents for which data were published in the County and City Data Books, in the entire United States in the period 1947-1977. Smallest Geographic Unit: county 2012-09-18 The data have been checked and corrected for inconsistencies, and have been reformatted to one record per case. SAS, SPSS, and Stata setup files have been updated. SPSS and Stata system files and a SAS transport (CPORT) file have been added to the collection. The codebook has been updated.2008-04-01 SAS, SPSS, and Stata setup files have been added to this data collection. record abstractsThis data file includes both state and county records. Records for counties in each state are listed immediately following the state record. All records have the same structure, and the identifier for each record includes both state and the county codes. In the state records, the county code is listed as 000.

Los Angeles is a city with a population of 3,855,442 and lies in the 3000-5000 (High) density category. The city has an area of 1044.45 km² with a total green space of 16% and a tree coverage of 13%. The city lies in the North Temperate Zone of the world. The city has improved its Urban green space per capita when compared to Global Average and also improved its Urban green space per capita when compared to previous year. Within North America, 9.1% of cities are ranked lower than Los Angeles.

The datasets are split by census block, cities, counties, districts, provinces, and states. The typical dataset includes the below fields.

Column numbers, Data attribute, Description 1, device_id, hashed anonymized unique id per moving device 2, origin_geoid, geohash id of the origin grid cell 3, destination_geoid, geohash id of the destination grid cell 4, origin_lat, origin latitude with 4-to-5 decimal precision 5, origin_long, origin longitude with 4-to-5 decimal precision 6, destination_lat, destination latitude with 5-to-6 decimal precision 7, destination_lon, destination longitude with 5-to-6 decimal precision 8, start_timestamp, start timestamp / local time 9, end_timestamp, end timestamp / local time 10, origin_shape_zone, customer provided origin shape id, zone or census block id 11, destination_shape_zone, customer provided destination shape id, zone or census block id 12, trip_distance, inferred distance traveled in meters, as the crow flies 13, trip_duration, inferred duration of the trip in seconds 14, trip_speed, inferred speed of the trip in meters per second 15, hour_of_day, hour of day of trip start (0-23) 16, time_period, time period of trip start (morning, afternoon, evening, night) 17, day_of_week, day of week of trip start(mon, tue, wed, thu, fri, sat, sun) 18, year, year of trip start 19, iso_week, iso week of the trip 20, iso_week_start_date, start date of the iso week 21, iso_week_end_date, end date of the iso week 22, travel_mode, mode of travel (walking, driving, bicycling, etc) 23, trip_event, trip or segment events (start, route, end, start-end) 24, trip_id, trip identifier (unique for each batch of results) 25, origin_city_block_id, census block id for the trip origin point 26, destination_city_block_id, census block id for the trip destination point 27, origin_city_block_name, census block name for the trip origin point 28, destination_city_block_name, census block name for the trip destination point 29, trip_scaled_ratio, ratio used to scale up each trip, for example, a trip_scaled_ratio value of 10 means that 1 original trip was scaled up to 10 trips 30, route_geojson, geojson line representing trip route trajectory or geometry

The datasets can be processed and enhanced to also include places, POI visitation patterns, hour-of-day patterns, weekday patterns, weekend patterns, dwell time inferences, and macro movement trends.

The dataset is delivered as gzipped CSV archive files that are uploaded to your AWS s3 bucket upon request.

Point data representing Airbnb listing for 44 cities across the world recorded between year 2015 - 2017. These listings are downloaded from Inside Airbnb (URL: http://insideairbnb.com/get-the-data.html), which is an independent, non-commercial set of tools and data that allow user to explore how Airbnb is being used in cities around the world.

This statistic illustrates the findings of a 2014 online survey among qualified utility, municipal, commercial and community stakeholders on which areas within their organizations would be best served by increased data management and analytics capabilities. According to ** percent of respondents, customer billing, collections and or revenue protection is among the top three areas to be served by such services.

The datasets were gotten from the chicago data portal: https://data.cityofchicago.org/. The City of Chicago's open data portal lets you find city data, lets you find facts about your neighborhood, lets you create maps and graphs about the city, and lets you freely download the data for your own analysis. Many of these datasets are updated at least once a day, and many of them are updated several times a day.

A dataset of global 3D city models including accurate building heights, roof geometries, topography, and infrastructure, aggregated from 150+ sources such as government data, LiDAR scans, and machine learning–derived estimates.

This is the complete dataset for the 500 Cities project 2016 release. This dataset includes 2013, 2014 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2013, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2009-2013, 2010-2014 estimates. More information about the methodology can be found at www.cdc.gov/500cities. Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

Content

Oil Usage (select City Owned Buildings), oil, city, data, statistics,heating, energy,heat

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated quarterly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Daniel Burka on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

A geographic depiction of city boundaries in Broward County, Florida.

City boundary data was last updated April 13th, 2021 and previously on February 22, 2021. A small edit was made between Tamarac and Fort Lauderdale just SW of the Executive Airport. In February adjustments were made between Pembroke Pines, Southwest Ranches and Cooper City where their geographies are coincidence and are meant to follow the northern boundaries of STR geography. Prior to this edit, the City of Coral Springs had annexed four parcels of land from unincorporated Broward County; Ordinances 2018-014 (1 parcel) and 2018-036 (3 parcels), effective Sept 15, 2019. Previously in May 2019, a correction was made to the boundaries of Southwest Ranches and Pembroke Pines at Dykes Road and Sheraton, just north of Sheraton, on the west side of Dykes. Prior to this change, a correction was made to the Lauderhill boundary at the Florida Turnpike interchange located at the Sunrise Blvd entrance on the east side of the turnpike in April 2019; the 1959 Lauderhill incorporation legal description, (Laws of Florida 59-1478) left this thirteen acre area as unincorporated. A 1994 boundary change between Plantation and Lauderhill, (Laws of Florida 94-427) de-annexed five parcels from Plantation and annexed them to Lauderhill in this area. However in 1996, Broward County's Strategic Planning and Growth Management Department made available data sets provided by Broward County’s Planning and Information Technology Division via a CD. This data set depicted this unincorporated area as being part of Lauderhill. This depiction remained such until a boundary adjustment in 2006-2007 incorrectly depicted this as being part of Plantation. In 2009 Broward County was made aware of this error and adjusted it partially using the CD boundary as a template. This resulted in the area being incorrectly assigned to Lauderhill. In September of 2018, Lauderhill revisited this boundary depiction by the County and in 2019 it was concluded this area is unincorporated following the 1959 and the 1994 boundary adjustment legal descriptions.

Prior to April 2019 there were other edits. The previous update of the data was Nov 7th, 2018, adusting the boundaries between Weston and Town of Davie to agree with House Bill 0871 which redefined a small area of their adjoining boundaries in the area of Weston Road and I-75. In July 2018, adjustments were made to the City of Margate to align with a city boundary shape file and written legal description as provided by John Shelton, GIS, City of Margate. The previous update was January 17th, 2018, correcting an unincorporated boundary line of the Triple H Ranch plat area within Parkland. This also reflects an adjustment made to Pembroke Pines southwest boundary between the Turnpike and SR 27 and the Sept 15th 2016 annexations of County unincorporated lands by Parkland. (City Ord 2016-06) and Coconut Creek (City Ord. 2015-027).Also a correction to the Hollywood/Davie boundary in the vicinity of Davie Blvd Ext and N 66 Ave and Oak St, per the City of Hollywood. Recent past boundary changes include annexations of county land to Pembroke Pines and Cooper City in 2015. And a Weston-Davie boundary adjustment in 2015; HB 871. And a July 2015 official resurvey of the City of Fort Lauderdale's boundaries which thus included adjustments to Oakland Park and Pompano Beach boundaries, (F. Gulliano, BC Engineering, M. Donaldson PSM, Fort Lauderdale). Also in 2015, a boundary adjustment was made to the eastern most boundary of Pompano Beach to match it to a more accurate depiction of the coastal erosion line by Broward County; (requested by the city to match their legal description). Further back, the were annexations for Parkland (2013) and Sunrise (Nov 2012) and updates to Lauderdale Lakes (per J. Petrov - BC Engineering 2012) and Plantation (I Reyes, GIS - Plantation 2012).

Source: BCGIS

Effective Date:

Last Update: 04/15/2021

Update Cycle: As needed.

This dataset is a daily export of all moving truck permits issued by the city. Both the raw data and the interactive map are updated daily with the latest available data.

Please note that not all permit locations in the raw data can be geocoded automatically, and these permits are therefore not included in the interactive map. These permits are still included in the tabular dataset.