How Mapalizer Works

Mapalizer's main purpose is to inform users about an unknown location by means of personalized preferences.
Mapalizer has 10 main groups and each group has its own sub-groups. The main groups are:
Nature & Parks
Health & Safety
Sports & Recreation
Shopping & Entertainments
Food & Beverage
Arts & Culture
Places of Worship
Simply, Mapalizer calculates the scores on map for each main group and shows weighted average of these scores.
The choices of the users'/visitors' determine the weights in calculations.
Let's start analyzing the stsyem from single amenity to final result.

Step 1, Single Amenity:

Let's try to show how important just one amenity (e.g. a bus stop) would be while deciding to buy/rent a property. "Bus stop" sub-group is placed under "Transportation" main group. If you try to draw its importance on the map, it will look like:
Note: We've hidden the background map images to see the result clearly.
Single Amenity Score In Mapalizer

Color layers shows the scores from high (dark) to low (light). A property cannot have 100 scores for "Transportation" group if there is a bus stop nearby.
Because there may be another property which is located not only nearby a bus station but also a railway station (another sub-group). So, it should have higher scores in "Transportation" group.
Score layer result sample for 2 different amenity

Therefore, sub-groups covered by a main group ("Transportation") must have a max score less than 100.
The calculated score is also inversely proportional to distance from origin. So each sub-group has its own max-distance where the score is zero.
The importance of a sub-group defines it's max score.

Step 2, Main Group Score:

Let's calculate the total score for "Transportation" main group. We are adding each independent sub-group in "Transportation" main group.
It looks like:
Resultant score for all sub-groups under Transportation

Step 3, Combine Groups:

Now, let's think weighted average of 2 groups. For instance, "Transportation" and "Nature & Parks".
To make this clear, we have disabled all other groups on Mapalizer and took screenshots by changing weights step by step from "Transportation" to "Nature & Parks".
Please see the weights (stars on right side) and result on the map.
Start transition:

Step 4, Combine All Groups:

Mapalizer calculates and shows the weighted average of 10 main groups.

We suggest you select only one main group while testing the system to understand what happens during the process.

Additional Notes

* Our starting point is explained in this blog post.
* We are using up-to-date OpenStreetMap data in calculation of score layers.
* For simplicity we reduced the number of main groups as much as possible.
* We also avoid giving more customization options to sub-groups to keep simplicity.
* We've extracted hundreds of sub-groups from "OpenStreetMap" features list, valuable for a real estate. Then we carefully determined the max-scores and max-distances of each subgroup to get the most reliable result.
Mapalizer's approach has a potential to be used in other areas/industries.
If you think it may be valuable in your field, please contact us.
Best wishes,
Mapalizer Team.