“Do you have some external data sources that we can add in our analysis?”

A question like us from our clients isn’t uncommon or alien to us.

What is External Data?

Internal data is mainly the information generated within the organization’s ecosystem and includes data like customer’s transactional data, human resource data etc.

A single source of data may not always capture the whole picture. External geospatial data streams refer to data that can be found and collected from other organizations publicly hosted data portals (transactional data, demographic data, etc).

Data related to affluence, demographics, lifestyle and movement pattern of people around particular geography come under the heading of alternative data and help organizations know their customers well in addition to their company-related data such as CRM information.

External data are derived data which are not easy to access. However, the top organizations in the world these days are leveraging external data to gain deeper and newer insights to stay ahead.

As per the survey by Clutch, a B2B research firm, companies when collecting and analyzing data most often focus on their organizational internal data, (70% of the time).

If you are:

  • A retail company asking “How do we find data about the income of the people at place X for their expansion purpose” or
  • An insurance company asking “how do we pinpoint the right areas that are prone to natural disasters to focus our marketing strategies on?” or
  • A food delivery or a restaurant company asking “can we use weather data to predict rains so that we are better prepared for the drop in demand?”

then this is the blog for you. We realized the value that external data have encompassed in them and therefore brainstormed on how such external sources can be of use for various industries that have operations in the chaotic and fickle real world.

Understanding business in relation to space, understanding where things happen, why they happen there, and where and how they will happen in the future, will become an essential tool for planning in future — Javier De La Torre , Founder , CART

Examples of External Data

Below, we talk about the different examples of external data sources along with their use cases.

Human Mobility

Human mobility plays a great role in uncovering human behavior patterns and individual habits. In physical space, human mobility refers mainly to the trajectories of human movement. The data sources of human mobility data are mainly GPS data, cell tower data, and WiFi data.

Human mobility can help to have insights on spatial distribution on movements, creation of user personas, use of public transportation, level of walkability, and pollution emission in a region.


The segregation of the various human mobility data are as follows:

  • Raw Data: Device latitude and longitude
  • Activity related: Number of visitors
  • Home and Work: Number of residents and workers.

Human mobility can help organizations in two ways :

  1. Ambient population/average footfall: Tracking real-time foot traffic can help to have a clear idea about the average expected population in an area in a day.
  2. Road/Rail Connectivity: This gives an idea about how the population in a particular region usually mobilize. This could be either show the density of roads or the rail connectivity in the particular region.

Use Cases

An interesting use case of human mobility data that organizations leverage every often is using real-time human traffic data to position billboards at the right location.

Human mobility data is also being used to manage the influx of micro-mobility. For example, In the metropolitan city of Nashville, city officials are coupling geospatial data with mobility data to better understand the concentration of vehicles and then re-distribute the immobile ones to areas where residents live rather than bringing in more vehicles on the roads. [Source]

KeyMe, a technology-enabled company working to revolutionize the locksmith industry using robotics used a real-time foot traffic model for site selection. As a result of leveraging location data the cohorts of newly installed kiosks resulted in a 52% increase in the average revenue than the ones which were installed previously.

Economical/Financial Data

Geospatial analysis plays a very important role in the banking industry, especially for financial service providers. Internal data of the banks combined with geospatial data gives insights that help them scale their business as well as serve their customers better.

The level of economic development is an indicator of expansion activity for many businesses. Income and affluence of people (i.e, the purchasing power) help companies figure out which brands or commodities should be offered to the population residing in a particular region.

MasterCard helps organizations better understand spending trends geographically over time. The solution provides insights into the performance of total monthly sales, average ticket size, the total number of transactions, performance stability and growth over time. [Source]


Affluence and income level can be extracted by parameters such as :

  1. Infrastructure: The average area of buildings in a region or the number of people residing in a particular circumference.
  2. Points of Interest: The number of ATMs, shops, restaurants, general stores in an area to which a given customer has access to
  3. Competitive Advantage: Business activity and development can be tracked and hence gives you an insight into who your competitor would be with your current customer base.

Property loans are evaluated by the information regarding the surrounding commercial property values and also have a good idea about the risk and the probability of loan repayment.

Use Cases

Using the affluence and the income level of different areas, businesses can also identify the areas they should expand into. In Kenya, mobile network operators are partnering with banks to create digital financial services delivered through mobile money. [Source]

Starbucks using a bunch of these factors to plan their next store location. You can read more here:

Site Planning by Starbucks using Location Intelligence
How Starbucks uses Location Intelligence to plan their next store location. Let’s see how they do it and what are some additional parameters that they consider.

Here’s a short video to understand how financial data and geo-spatial data when leveraged together help the banking industry in their day-to-day activities .


Demographics data help us to describe and quantify characteristics of a populace in a region. It can be used spatially to highlights patterns that can help answer the strategic business questions.

Analyzing demographics in an area helps us find the answer to some of the challenging questions like — How old is the population of a place? How educated is the population at a place? How is the population varying over time?


Two major factors which highlight the demography at a place are as follows:

1. Age/Gender: Helps to understand the percent of the population belonging to a particular age/gender slab.

2. Residential Population: Gives an estimate of the number of people residing in a particular area.

An IMF info-graphic showing the demographic change of population in Japan [Source]

Use Cases

The ultimate success of any data-driven marketing strategy hinges on an organization’s ability to collect the right data and how they leverage the combination of demographic and location data. Industries such as hospitality and tourism are heavily dependent on demographic trends.

Hospitality companies can identify countries that are showing the most demand for travel services. This helps them invest their marketing budgets to reach customers in those regions [Source]

.id, a company focused on economic, population and demographic forecasting helped school planners to zoom in and pinpoint suburbs where growth in school children will occur over the next ten years. This helps school planners to strategically locate the type of school (play-school, high-school, etc) as per the average age of the population at that place.

Orbital Insights teamed up with the Arab Human Development Report team at the United Nations Development Program for a mission to eradicate poverty and reduce inequality mainly focusing on slums dwellers in Cairo, Eygpt.

Location data (aggregated location data sourced from smartphones) combined with data about the slum boundaries gave insights about the aggregated behavior of the devices and in turn information about the population residing within that boundary.

Environmental/Weather Data

Weather/Environmental data plays a strategic role in industries such as real estate, logistics, food delivery and sharing economy. Weather/Environmental data combined with internal data can give patterns that will help organizations take adequate measures for their business.

Foodtech company Deliveroo leverages weather data along with its internal data to handle swings in demand during extreme weather conditions. They keep themselves prepared for any variation in conditions even if it just a millimeter of rain. [Source]

Weather is the largest external swing factor in the U.S economics and accounts for over $550 billion per year in lost revenue and up to 76,000 lost jobs.

Parameters such as weather hazard risk index play a very important role in the real estate industry. Rates for properties that are in high-risk zones are normally appreciated.

In the sharing economy (Uber/Airbnb), the demand for services fluctuates with respect to sudden changes in weather conditions (sudden rain, gusty storm). The prices also surge to meet the existing demand and to balance out the demand and supply of drivers and riders respectively.

Satellite Imagery

Satellite imagery is also a common source of a lot of rich, external insights. Satellites these days are providing unprecedented near real-time view of every corner of the earth, from the wheat-fields in Egypt to the glaciers in Antarctica.

One key advantage of satellite imagery is that they provide faster image delivery and hence finding a multitude of applications in the commercial space.


Satellite imagery is classified on the basis of the time of the day during which they can be used and the suitable conditions required for them to operate optimally. They are of the following three types:

  • Visible: Visible satellite imagery can only be viewed during the day since clouds reflect the light from the sun. They are useful in predicting thunderstorm cloud formation since the cloud can be identified by white color much before they are detected by radars.
  • Infrared: One advantage that infrared satellite imagery has over visible satellite imagery is that they can be viewed both during the day and night. Using infrared imagery foggy weather and low cloud can be detected.
  • Water Vapor: Water vapor satellites indicate the level of moisture present in the atmosphere. They are widely used to predict where heavy rain is possible.

Use Cases

One of the interesting satellite data that we love at Locale is nightlights — that is a good proxy for the economic growth and development happening in an area. Nightlights are captured by NASA and measures the nighttime activity of very granular areas every single day. If you want to analyze how fast different cities are growing or which city should you expand into, nightlights is a powerful data source for that.

You can read more about nightlights here:

Illuminating human activities using the Night Light
Today our nights are getting brighter than ever and the reason is that we have lights everywhere. Our markets, billboards, roads, and homes are lit. Every place humans have inhabited has some sort…

High-quality satellite imagery is being vastly used to forecast crop yield and land cover classification. Not only that satellite imagery is used for remote sensing which helps organizations to better serve rural segments in terms of agricultural service, micro-finance service, and off-grid energy transmission. Satellite imagery is also used for damage assessment after disaster and calamities. Object-based image classification using change detection (pre and post-event) is a quick way to get damage assessments.

In commercial space, satellite imagery is used for many purposes. One interesting use case is how the profitability of a retail store is predicted by counting the number of cars parked in the parking [Source]

You can read more on satellite imagery and spatial analysis here:

The Five Most Genius Applications of Spatial Analytics for Businesses
For all the business decisions that have the “where” question.

Points of Interest

Points of interest are a dedicated geographic entity such as a place of worship, heritage site or corporate office. POI data is most often used to assess a location and analyze the behavioral trends of the public. It can also be used by businesses to monitor the site whereabouts and some of the marketing strategies of their competitors.

Organizations use location intelligence for site selection to pinpoint areas that have market potential. By tracking past and present activity in their area of interest company’s can decide which outlets are profitable and which outlets have a relatively less market share.

POIs have the most versatile use case. They can be used by tourism agency to highlight attractions and activities happening in and around in the city or by a university to pinpoint locations on campuses for students and visitors or for that matter by an individual to set their own POIs like “Work” or “Home” in Google Maps for easy navigation.

Use Cases

Orbital Insights platform GO helped drive site selection for gas stations by tracking gas demand at the granular level of individual gas stations. Not only that, but data about points of interest also finds its use in logistic optimization.

Esri helps logistic companies in route planning by increasing the number of deliveries in a route and by directing fleets to routes that have filling stations on the way.


Here are a couple of sources to find external data of various types. You can find many other sources of external open data on the internet .

  1. Human Mobility data: https://bit.ly/2HGFzUX
  2. Financial/Economical data: https://data.worldbank.org
  3. Demographics data: https://data.gov.in/keywords/population
  4. Environment Data: https://www.data.gov/weather/
  5. Points of Interest Data: http://www.poi-factory.com/

The coupling of different data sources along with geospatial data is helping organizations to find solutions to some complex problems and make data-driven decisions. It might sound astonishing but factors that probably might seem to have no influence can help bridge gaps and also scale an organization's business.

Similar Reads :

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If you want to delve further, check our website out or get in touch with Aditi Sinha on LinkedIn or Twitter.