US State New Jersey Municipalities With Geoname IDs


Files Size Format Created Updated License Source
2 299kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Wikipedia

Data Files

File Description Size Last changed Download
us-state-new-jersey-municipalities-with-geoname-ids-csv 45kB csv (45kB) , json (184kB)
us-state-new-jersey-municipalities-with-geoname-ids_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 45kB zip (45kB)


This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Municipality_Geoname_ID 1 integer The unique identifier in the GeoNames geographical database
Rank_By_Population_Size 2 integer The rank of a municipality among New Jersey municipalities, according to population size
Municipality 3 string Municipality's name
County 4 string The name of the county where the municipality is localized
Population_Size_In_2010 5 integer The population size of a municipality according to the 2010 census
Municipality_Type 6 string One of the five types of municipalities met in New Jersey (borough, city, town, township and village)
Form_Of_Government 7 string The form of government the municipality employs
Community_Establishment_Year 8 date (%Y-%m-%d) The year when first community has been established on the current municipality area
Community_Incorporation_Year 9 date (%Y-%m-%d) The year when the community established on the current municipality area, incorporated in form of locality
Notes 10 string Information regarding change of name or type of municipalities, along with the year the change occurred

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the almost like you use git with the github. Here are installation instructions.

data get
tree JohnSnowLabs/us-state-new-jersey-municipalities-with-geoname-ids
# Get a list of dataset's resources
curl -L -s | grep path

# Get resources

curl -L

curl -L

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="")

json_file <- ''
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = ''

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('')

# print list of all resources:

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = ''

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data