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.
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.
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:
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.
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.
* 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.