Optimal GeoSpace Report Settings Overview

Oftentimes new users wonder how each of the Optimal GeoSpace report settings will affect their trade area shape. Here is a study on the effect of each setting on four H&M sites in Singapore during the ‘last 6 months’.

Specify site: H&M 

Specify location: Four Locations:

  1. H&M Suntec City, 3 Temasek Blvd 307-311 Suntec Suntec City, Singapore 38983
  2. H&M Tampines Mall, 4 Tampines Central 5 02-25/26 [level 2] 03-24/25 Tampines Mall Tampines Mall, Singapore 529510
  3. H&M Nex, 23 Serangoon Central Unit #01-12 to #01-33 Central Singapore, Singapore 556083
  4. H&M Orchard, 2 Orchard Turn #01-12 ION Orchard ION Orchard, Singapore 238801

For generating Optimal GeoSpace report, a user gets the following options:

Let’s now explore each setting:

Optimal GeoSpace Input type 


This setting can be used to find out where a customer was before or after they visited a location by designating a time frame in which to look for them. The Before and After values do not have to be the same, but must be any whole number greater than or equal to 0.

Time Period

Depending on the use case, these settings can be modified. For our study, we have kept the settings as 20 min before and after. 

If you are only interested in the whereabouts of your visitors after they visited your site, you can keep the “before” time as zero and only provide input for the “after” time and vice-versa.

We strongly recommend to utilize the ‘Path-To-Purchase” setting for determining the trade area as this will be able to provide a tighter Optimal GeoSpace (OGS) shape. If left unchecked, the resulting OGS would be wider and might not provide accurate representation. 

Common Evening Location

Select this setting if you want to include the data around where the visitor’s spend their evening time and on weekends (generally a proxy for home location)

Common Daytime Location

Select this setting if you want to include the data around where the visitor’s spend their weekday hours time (generally a proxy for work location)


For maximum robustness, Near recommends to leverage all three input types. However, a specific analysis depending on use cases may inspire using only CEL or CDL data.

Use Equal Weights

This setting, when toggled on, minimizes the impact of a subset of high-emittance devices which create a significant amount of pathing points. If the ‘Path-To-Purchase’ input type is not used, then this switch will have no impact on the final OGS shape. 

It is recommended to use ‘Equal weight’ when ‘Path-To-Purchase’ input type is used.

Minimum Number of Visitors

This is the minimum number of visiting devices required to define an OGS. Near has set a default minimum count of 38 devices but this can be modified on a per use case basis. This number can be seen as a confidence indicator, where more devices will more often exhibit the average behavior and thus be a better indicator of overall catchment.

% of Data Retained

Set the amount of data points from visitors found in the polygon to develop the Optimal GeoSpace contour. Retaining a smaller percentage of Optimal GeoSpace data will reduce the size of the Optimal GeoSpace.

The maximum and minimum values that can be selected are 60% and 85% respectively. The recommended value is 70%

Impact of different settings 

Below are the sample outputs based on the different settings to show the impact each setting may have on the overall OGS. For this study we have taken 4 H&M stores in Singapore and are analyzing the OGS/trade area for cannibalization analysis. This study simply serves the purpose of visually representing the impact of various settings on the output OGS. 

OGS generated with only CEL and CDL settings (excluding the Path-To-Purchase checkbox)

Settings snapshot:

Output Snapshot:

As you can see, without the Path-To-Purchase input, the trade areas are larger and may not represent the actual picture.

OGS generated with all inputs 

Settings snapshot:

Output Snapshot:

With all the recommended settings, the output trade area is more precise and this can now be used to analyze cannibalization.

OGS generated with % data retained as 60%

Settings snapshot:

Output Snapshot:

With the % data retained as 60%, the trade area shrinks further. 


As can be seen from above examples, it is very important to realize what settings to use while creating an OGS report. Please use the settings best fit for your use case. For recommended setting values, please refer here.