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Tag: geocoding

Calculating the Distance between Two Locations Using Geocodes

PYTHON. GEOSPATIAL ANALYTICS. LOCATION DATA.How to use Python to calculate the distance between two sets of geocodesPhoto by Tamas Tuzes-Katai on Unsplash

You’ve heard the famous phrase “Location, Location, Location” for instances where people want to emphasize the centrality of the location to business and real estate.

In data analysis and computing, however, this phrase is a bit ambiguous. The way computers understand the concept of “location” is through what we know as “geocodes.” These are the longitudes and latitudes and are specific to a particular location.

Note: For those who want to know how to calculate these geocodes, I have written an article regarding this.Read more...

Geospatial Data File Format Conversions (KML, SHP, GeoJSON)

Use these JavaScript utilities. No cost. No installation. No quota. (HTML File included)

Screenshot by Author | The typical popup window I face when I attempt to convert a spatial data file with online utilities

Generating Geocodes Using Google Maps API


Getting Geocode of an Address Quickly Using Python

Geocoding is the process of translating text addresses into geographic latitude and longitude coordinates which in turn make it easy to manipulate and analyze massive amounts of geospatial data.

For a data scientist, knowing the geocode makes it easy to plot it in visualization and create other features, such as distances and time differences between two points.

Unlike other types of data, geospatial data benefits greatly from visualization as it makes apparent the occurrence of patterns in neighborhoods or local networks.

As such, businesses profit from the use of it but not if the process involves manually getting the geocodes.


Geospatial Analytics for Reassessing Urban Structures

Utilizing Geohash and data science models to solve traffic congestion and spatial inequality that has resulted from the current urban structure

Image from

In today’s technological era, location data has been essential for the business operations of several tech companies. By enabling users to link device location of the firm’s platform, data teams can build models and insight reports using the available data. Such cases can be price setting based on a location’s demand or visualizing sales coverage by city. While working with such data, I cannot help but imagine potential use cases beyond business. As an urban enthusiast, the first use case that came to mind is generating insights for tackling traffic congestion and spatial inequalities within an urban area.


Geospatial Modeling: The Future of Pandemic Analysis


  • Geospatial modeling may be the future of pandemic control.
  • Recent studies analyzed local data and found hidden trends.
  • Border control isn’t enough to stop the spread of Covid-19.
  • Where you live determines your risk for the disease.

Significant amounts of data have been collected, analyzed, and reported globally since the start of the Covid-19 pandemic, leading to a better understanding of how the disease spreads. Much of this data has been analyzed with geospatial modeling, which finds patterns in data that includes a geospatial (map) component. The modeling technique uses Geographic Information System (GIS), originally developed in the 1960s to store, collate, and analyze data about land usage [1].