This dataset is about cities in the United States. It has 4,171 rows. It features 7 columns including country, population, latitude, and longitude.

This dataset includes basic data about all US cities with a population over 100.000 (333 cities)

Source: https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population

Coordinates of cities have been geocoded using https://rapidapi.com/GeocodeSupport/api/forward-reverse-geocoding/

Rows description:

City: Name of city State: Name of state Latitude, Longitude, Population_estimate_2022: Estimated population in 2022 Population_2020: Population figure from 2020 census Change_population: % change in population between 2022 and 2020 Land_area: City land area in sq. mi. Population_density_2020: density of population per sq. mi. in 2020

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

Abstract

A crosswalk table from US postal ZIP codes to geo-points (latitude, longitude)

Documentation

Data source: public.opendatasoft.

The ZIP code database contained in 'zipcode.csv' contains 43204 ZIP codes for the continental United States, Alaska, Hawaii, Puerto Rico, and American Samoa. The database is in comma separated value format, with columns for ZIP code, city, state, latitude, longitude, timezone (offset from GMT), and daylight savings time flag (1 if DST is observed in this ZIP code and 0 if not).

This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources. The latitude and longitude given for each ZIP code is typically (though not always) the geographic centroid of the ZIP code; in any event, the location given can generally be expected to lie somewhere within the ZIP code's "boundaries".The ZIP code database contained in 'zipcode.csv' contains 43204 ZIP codes for the continental United States, Alaska, Hawaii, Puerto Rico, and American Samoa. The database is in comma separated value format, with columns for ZIP code, city, state, latitude, longitude, timezone (offset from GMT), and daylight savings time flag (1 if DST is observed in this ZIP code and 0 if not). This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources. The latitude and longitude given for each ZIP code is typically (though not always) the geographic centroid of the ZIP code; in any event, the location given can generally be expected to lie somewhere within the ZIP code's "boundaries".

The database and this README are copyright 2004 CivicSpace Labs, Inc., and are published under a Creative Commons Attribution-ShareAlike license, which requires that all updates must be released under the same license. See http://creativecommons.org/licenses/by-sa/2.0/ for more details. Please contact schuyler@geocoder.us if you are interested in receiving updates to this database as they become available.The database and this README are copyright 2004 CivicSpace Labs, Inc., and are published under a Creative Commons Attribution-ShareAlike license, which requires that all updates must be released under the same license. See http://creativecommons.org/licenses/by-sa/2.0/ for more details. Please contact schuyler@geocoder.us if you are interested in receiving updates to this database as they become available.

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.

This dataset is about cities in Northern America. It has 4,891 rows. It features 7 columns including country, population, latitude, and longitude.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

Relates US cities to their ASHRAE Climate Zones. 28838 cities are listed with their state, county, zip, ASHRAE zone, Longitude, Latitude, Population, Density, Military and Incorporation Statuses

Sustainable cities depend on urban forests. City trees -- a pillar of urban forests -- improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about urban forests as ecosystems, particularly their spatial composition, nativity statuses, biodiversity, and tree health. Here, we assembled and standardized a new dataset of N=5,660,237 trees from 63 of the largest US cities. The data comes from tree inventories conducted at the level of cities and/or neighborhoods. Each data sheet includes detailed information on tree location, species, nativity status (whether a tree species is naturally occurring or introduced), health, size, whether it is in a park or urban area, and more (comprising 28 standardized columns per datasheet). This dataset could be analyzed in combination with citizen-science datasets on bird, insect, or plant biodiversity; social and demographic data; or data on the physical environment. Urban forests offer a rare opportunity to intentionally design biodiverse, heterogenous, rich ecosystems. Methods See eLife manuscript for full details. Below, we provide a summary of how the dataset was collected and processed.

Data Acquisition We limited our search to the 150 largest cities in the USA (by census population). To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transformed to .csv format (if they were not already in that format). We received data on street trees from 64 cities. One city, El Paso, had data only in summary format and was therefore excluded from analyses.

Data Cleaning All code used is in the zipped folder Data S5 in the eLife publication. Before cleaning the data, we ensured that all reported trees for each city were located within the greater metropolitan area of the city (for certain inventories, many suburbs were reported - some within the greater metropolitan area, others not). First, we renamed all columns in the received .csv sheets, referring to the metadata and according to our standardized definitions (Table S4). To harmonize tree health and condition data across different cities, we inspected metadata from the tree inventories and converted all numeric scores to a descriptive scale including “excellent,” “good”, “fair”, “poor”, “dead”, and “dead/dying”. Some cities included only three points on this scale (e.g., “good”, “poor”, “dead/dying”) while others included five (e.g., “excellent,” “good”, “fair”, “poor”, “dead”). Second, we used pandas in Python (W. McKinney & Others, 2011) to correct typos, non-ASCII characters, variable spellings, date format, units used (we converted all units to metric), address issues, and common name format. In some cases, units were not specified for tree diameter at breast height (DBH) and tree height; we determined the units based on typical sizes for trees of a particular species. Wherever diameter was reported, we assumed it was DBH. We standardized health and condition data across cities, preserving the highest granularity available for each city. For our analysis, we converted this variable to a binary (see section Condition and Health). We created a column called “location_type” to label whether a given tree was growing in the built environment or in green space. All of the changes we made, and decision points, are preserved in Data S9. Third, we checked the scientific names reported using gnr_resolve in the R library taxize (Chamberlain & Szöcs, 2013), with the option Best_match_only set to TRUE (Data S9). Through an iterative process, we manually checked the results and corrected typos in the scientific names until all names were either a perfect match (n=1771 species) or partial match with threshold greater than 0.75 (n=453 species). BGS manually reviewed all partial matches to ensure that they were the correct species name, and then we programmatically corrected these partial matches (for example, Magnolia grandifolia-- which is not a species name of a known tree-- was corrected to Magnolia grandiflora, and Pheonix canariensus was corrected to its proper spelling of Phoenix canariensis). Because many of these tree inventories were crowd-sourced or generated in part through citizen science, such typos and misspellings are to be expected. Some tree inventories reported species by common names only. Therefore, our fourth step in data cleaning was to convert common names to scientific names. We generated a lookup table by summarizing all pairings of common and scientific names in the inventories for which both were reported. We manually reviewed the common to scientific name pairings, confirming that all were correct. Then we programmatically assigned scientific names to all common names (Data S9). Fifth, we assigned native status to each tree through reference to the Biota of North America Project (Kartesz, 2018), which has collected data on all native and non-native species occurrences throughout the US states. Specifically, we determined whether each tree species in a given city was native to that state, not native to that state, or that we did not have enough information to determine nativity (for cases where only the genus was known). Sixth, some cities reported only the street address but not latitude and longitude. For these cities, we used the OpenCageGeocoder (https://opencagedata.com/) to convert addresses to latitude and longitude coordinates (Data S9). OpenCageGeocoder leverages open data and is used by many academic institutions (see https://opencagedata.com/solutions/academia). Seventh, we trimmed each city dataset to include only the standardized columns we identified in Table S4. After each stage of data cleaning, we performed manual spot checking to identify any issues.

Latitude and longitude coordinates, population size, and mean baseline pneumonia and influenza death rates for 66 large US reporting cities (1910–1920) with 100, 000 or more inhabitants [10].

Complete dataset of all 29,850 USA cities Roads network as a graph in the shp format. The extracts follow 2016 official USA cities boundaries. Graph are identified by their [city_code].shp. Cities code are provided by the Tiger Census Dataset. Graph have been created by extracting all openstreetmap.org (osm) maps for each USA Cityextracting the graph from osm extract using the policosm python github librarysimplifying the graph by removing all degree two nodes to retain only a workable transportation network. Original road length is retained as an attribute Nodes includes latitude and longitude attributes from WGS84 projection Edges includes length in meter (precision < 1m), tag:highway value from osm See policosm on github for more informations on extractions algorithm

This dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) for all complete quarters so far this year (2017). For additional details, please see the attached data dictionary in the ‘About’ section.

This Wyoming Cities coverage contains data for 109 Wyoming towns, cities, and Census Designated Areas. The coverage was created from U.S. Census Bureau Tiger Data. It contains many of the same attributes as the Census Bureau .dbf files, but there are a few modifications. A code has been added to distinguish towns, cities, and CDPs. Also a Countyseat item has been added to provide a way to display the County Seats only. Latitude/Longitude coordinates were also converted from the Census Bureau format to be used in GENERATE.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

cities continent America. name, office head of government, Mayor, image, Area, date founded, Elevation, Country, administrative division, continent, latitude, waterbody, longitude, Website, population, Demonym

Content

This database is a collection of data over 23,000 US earthquakes. It contains data from the year 1638 to 1985. The digital database also includes information regarding epicentral coordinates, magnitudes, focal depths, names and coordinates of reporting cities (or localities), reported intensities, and the distance from the city (or locality) to the epicenter. The majority of felt reports are from the US but there is information about also some other countries such as Antigua and Barbuda, Canada, Mexico, Panama, and the Philippines.

Columns Description

Year Mo Da Hr Mn Sec The Date and Time are listed in Universal Coordinated Time and are Year, Month (Mo), Day (Da), Hour (Hr), Minute (Mn), Second (Sec)

UTC Conv Number of hours to subtract from the Date and Time given in Universal Coordinated Time to get local standard time for the epicenter. In general: 4 = 60 degree meridian (Atlantic Standard Time) 5 = 75 degree meridian (Eastern Standard Time) 6 = 90 degree meridian (Central Standard Time) 7 = 105 degree meridian (Mountain Standard Time) 8 = 120 degree meridian (Pacific Standard Time) 9 = 135 degree meridian (Alaska Standard Time) 10 = 150 degree meridian (Hawaii-Aleutian Standard Time)

U/G Unpublished or grouped intensity U = Intensity (MMI) assigned that was not listed in the source document. G = Intensity grouped I-III in the source document was reassigned intensity III.

EQ Lat / EQ Long This is the geographic latitude and longitude of the epicenter expressed as decimal numbers. The units are degrees. The latitude range is +4.0 to +69.0, where "+" designates North latitude (there are no South latitudes in the database). The longitude range is -179.0 to +180.0, where "-" designates West longitude and "+" designates East longitude. Most of the epicenters are West longitude (from -56 to -179), but a few epicenters in the Philippines and Aleutian Islands are East longitude (from +120 to +180).

Mag These are magnitudes as listed in United States Earthquakes, Earthquake History of the United States (either mb, MS, or ML), or the equivalent derived from intensities for pre-instrumental events. The magnitude is a measure of seismic energy. The magnitude scale is logarithmic. An increase of one in magnitude represents a tenfold increase in the recorded wave amplitude. However, the energy release associated with an increase of one in magnitude is not tenfold, but thirtyfold. For example, approximately 900 times more energy is released in an earthquake of magnitude 7 than in an earthquake of magnitude 5. Each increase in magnitude of one unit is equivalent to an increase of seismic energy of about 1,600,000,000,000 ergs.

Depth (km) Hypocentral Depth (positive downward) in kilometers from the surface.

Epi Dis Epicentral Distance in km that the reporting city (or locality) is located from the epicenter of the earthquake.

City Lat / City Long This is the geographic latitude and longitude of the city (or locality) where the Modified Mercalli Intensity was observed, expressed as decimal numbers. The units are degrees. The latitude range is +6.0 to +72.0, where "+" designates North latitude (there are no South latitudes in the database). The longitude range is -177.0 to +180.0, where "-" designates West longitude and "+" designates East longitude. Most of the reporting cities (or localities) are West longitude (from -29 to -177), but a few reporting cities (or localities) in the Philippines and Aleutian Islands are East longitude (from +119 to +180).

MMI Modified Mercalli Scale Intensity (MMI) is given in Roman Numerals. Values range from I to XII. (Roman Numerals were converted to numbers in the digital database. Values range from 1 to 12.) Macroseismic information is compiled from various sources including newspaper articles, foreign broadcasts, U.S. Geological Survey Earthquake reports and seismological station reports.

State Code Numerical i identifier for state, province, or country in which the earthquake was reported (felt) by residents: 01 Alabama 02 Alaska 03 Arizona 04 Arkansas 05 California 07 Colorado 08 Connecticut 09 Delaware 10 District of Columbia 11 Florida 12 Georgia 14 Hawaii 15 Idaho 16 Illinois 17 Indiana 18 Iowa 19 Kansas 20 Kentucky 21 Louisiana 22 Maine 23 Maryland 24 Massachusetts 25 Michigan 26 Minnesota 27 Mississippi 28 Missouri 29 Montana 30 Nebraska 31 Nevada 32 New Hampshire 33 New Jersey 34 New Mexico 35 New York 36 North Carolina 37 North Dakota 38 Ohio 39 Oklahoma 40 Oregon 41 Pennsylvania 42 Puerto Rico 43 Rhode Island 45 South Carolina 46 South Dakota 47 Tennessee 48 Texas 49 Utah 50 Vermont...

This dataset lists markets that feature two or more farm vendors selling agricultural products directly to customers at a common, recurrent physical location. It includes address, city, county, state and latitude/longitude information of the farm market of the USA. It also contains information about directions, operating times, product offerings, accepted forms of payment and contact information for each market as well as URLs of their websites and communities in the popular social networks.

dataplor’s location intelligence for the U.S. addresses a significant gap in location intelligence by combining precise POI data with accurate visitation counts. Traditionally, businesses rely on demographic or static market data, which often fails to capture the dynamic nature of consumer behavior. By including foot traffic insights, businesses can gauge not just the market potential of a location but also its real-world popularity and activity patterns. This enhanced perspective allows users to:

(1) Assess economic trends and evolving consumer interests. (2) Benchmark competitor performance with greater accuracy. (3) Pinpoint underserved market opportunities. (4) Develop precise, data-driven forecasts.

By providing GDPR-compliant, non-PII mobility data paired with the most comprehensive location data for the United States, our product ensures businesses can act with confidence while maintaining data privacy standards. dataplor's datasets include 55+ attributes such as:

• Unique, Static dataplor ID
• Business Name
• Main/Sub/Business Categories
• Chain ID/Name
• Home Address
• Neighborhood
• City, State, Postal, Country
• Latitude/Longitude
• Open/Closed Status
• Visit Counts
• Confidence Scoring
• First Opened
• Popularity Indices
• Sentiment Indices and more

About this Dataset

Contains complete list of all Audi Dealerships in the US Data contains Dealership Name, Address, City, State, Zipcode, Phone Number, Latitude, Longitude, Website URL

Category

Location Lists

Keywords

Audi,Dealership,Automotive,Address

Row Count

310

Price

$69.00

About this Dataset

Contains complete list of all Chevrolet Dealerships in the US Data contains Dealership Name, Address, City, State, Zipcode, Phone Number, Latitude, Longitude, Website URL

Category

Location Lists

Keywords

Chevrolet,Automotive,Dealership

Row Count

2909

Price

$69.00