US State New Jersey Municipalities With Geoname IDs


Files Size Format Created Updated License Source
2 299kB csv zip 1 week ago John Snow Labs Standard License John Snow Labs Wikipedia

Data Files


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


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

Read me

Import into your tool

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


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

# access csv file by the index starting from 1
path_to_file = json_data$resources$path[1][1]
data <- read.csv(url(path_to_file))

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = ""

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)

# you can access datasets inside storage, e.g. the first one:

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('')

# get list of resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]

data = package.resources[0].read()

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 the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = ''

package =
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data =
puts data