In the foreseeable future, 5G networks and LTE capacity additions will be built where the network congestion is the highest. Each carrier has the knowledge of where this needs to happen based on their internal data gathering from their customer traffic. Others would like to know this information as well but they don’t have access to this proprietary information. These planned networks do not follow census data. Census data is based on where people live and most information that results from the census ties it to your place of residence. The challenge these days is to know the mobility of society. Where are the hot spots of congregation, what are the heavy travel patterns, how do these patterns change based o the time of day? Where do people work, eat, shop and spend their free time?
Traditionally we have used highway traffic count data to gauge the mobility of society. It works well in absence of any other data, but it typically only shows the movements based on automotive activities. Other movement such as pedestrian, mass transit and such is not factored in. To measure and visualize today’s society movements, new data sources need to be utilized.
Enter the smart phone. For quite a few years now there has been a great deal of data collected from various smart phone applications that has location based information included. These data sources can be compiled and used in such a way to be able to show high and low traffic areas. Taking this information and converting it to easily understandable formats such as heat maps, one can understand where the mobility of society is, where the heavy traffic areas are, where people work, and where they spend their free time. Since the sensor data was the smart phone, this also shows the more heavily used portions of the mobile carrier networks. More data samples equates to more network use.
These maps can be looked upon as demand areas. While a lot of the areas mirror the same heavy use areas from highway traffic count data, there are other heavy use areas that don’t show using just traffic counts. This makes sense if you look at the demand in areas where there is likely more pedestrian and non vehicular traffic movement. There are also some high volume road traffic areas that show less mobile network use than the traffic hits would indicate. This makes sense if you think about the data demands. While driving most data used by a smart phone is pushed to it and not in large amounts. People are driving and don’t typically consume a lot of data such as video services.
As previously mentioned looking at mobile use compared to census data shows that network use is not directly tied to information such as households per square mile.
The level of detail examined in a particular market can change the granularity of use patterns in a network. Based on the data sample and geographic size of an area, different results can be shown. Being able to look at a large region is one thing but then recalculating the use patterns for a more focused area can show the use patterns relative to other areas within the smaller region.
This series of maps show network use patterns for the greater NY metro area and the change in granularity when looking at more focused regions.
They key to utilizing this type of data to create useful intelligence is understanding the sample size, especially as it relates to the geographic area. The analytic tools used interpolate the data points in complex ways and land area size relative to the data points is a major factor in the visual map results.
This information is useful as mentioned to learn where the likely first wave of 5G deployments will happen. There are many other uses of the information as well. For smart kiosk and smart city implementations, this data showing the mobility of society is key to locating hardware and services to benefit the most users. Many other industries can make use of this information as well. In a way it can be viewed of as a mobility census.
If you have a need to know this type of information and want to analyze it in conjunction with some of your own data please contact us.
The next post will discuss other uses for this type of data.