Use Scenarios

Create “Lifestyle Scores”

By combining relevant criteria, scores can be created to represent a more general lifestyle score, as opposed to individual metrics. For example: a Gastronomy score could be comprised of walkable café count and walkable restaurant count. Nightlife could be comprised of walkable bar/pub count, walkable clubs, population of 20-30 year-olds and at least one late-night alcohol shop. In addition to generic scores, criteria could be added or removed to reflect an user’s preferences, giving an hyper-personal result.

Rank and filter locations

To pick the best location between candidate locations, scores can be assembled to represent likelihood of success. To know which of 20 potential locations a new café should be located at, gravitation-based scores can be calculated to rank locations with lower nearby competition.

Glossary

Criteria

Criteria are the base measurements which Targomo uses to evaluate a location. They can be based on points of interest (nearest, count), statistics (basic summary stats, combined equations), or advanced mathematical expressions.

Scores

Scores are the measurements of individual criteria for a specific location. Scores are raw values, which represent the measurement method for the specific criteria. If the criteria were “count of reachable cafés”, the score might be “15”. If the criteria were “reachable population between 20-30 years”, the score might be “68,000”.

Ratings

The true value of the Location Scoring API is the ratings. Ratings are pre-defined collections of criteria, the scores of which are normalized and compared against reference areas (such as neighborhood, or county). These reference area have pre-calculated full-coverage, allowing for instant ratings for any location, on multiple reference resolutions.

Targomo’s Ratings are a custom offering - contact us for a demo of this powerful feature.

The BIG Picture

The Location Scoring API is an utility toolbelt for generating a system for location comparison. There are several ways one can generate comparative metrics for point data:

Closest Point of Interest

raw time/distance or distance-weighted.

Point of Interest Count

number of reachable POIs.

Statistics

true sum or distance-weighted sum of statistic field.

Gravitation-Based

generate POI or statistic scores considering the draw-potential for certain locations (competition/own-network).

Expression-Based

generate scores which are mathematical expressions combining and/or comparing several criteria.

Made to be used together

Getting individual scores are less interesting than the ability to combine many criteria together. What makes a location “good”? It can be very different by industry, and can also be influenced by the specific interests of an user - instead of only having global scores available, our approach is the give you the tools to assemble your own “scores”.

Basis for comparison

The results from each criteria can be on completely different data scales - a “good” score for a distance-weighted closest POI is 1, while a good score for a reachable population statistic sum could potentially be in the hundreds-of-thousands. In order to combine the criteria results, you can normalize the scores across your dataset - the highest-scoring location will be a 1, and the lowest scoring location will be a 0. This will result in a ranking for all locations by criteria, and these rankings can be combined to represent the an overall score. E.g.:

individual_score = (actualValue - minVal) / (maxVal - minVal)
combined_score = (score1 * weight1) + (score2 * weight2) + (score3 * weight3) / (weight1 + weight2 + weight3)

Inputs

Basic inputs (see docs for more)

• locations: array of points to generate score for
• criteria: object of criteria which will be analyzed (Point of Interest, Statistics, Statistics Gravitational, or Poi Gravitational)
  • Point of Interest: closest or coverage (true or distance-weighted)
  • Statistics: true sum or distance-weighted sum of statistic field
  • Statistics Gravitational: gravity-model influenced statistics field
  • Poi Gravitational: gravity-model influenced point of interest coverage
• competition: array of starting points - useful for gravitation-based criteria

Outputs

Basic output (see docs for more details)

{
    "data": {
        "Location1": {
            "id": "Location1",
            "lat": 52.481577,
            "lng": 13.434219,
            "scores": {
                "cafe": 304,
                "g_eat-out": 790
            },
            "details": {...}
        },
        "Location 1": {...},
        "Location 2": {...}
    },
    "message": "Scores calculated",
    "errors": [],
    "timestamp": "17-09-2021 08:21:37"
}

Operations counting strategy for public use

The Location Scoring API is a convenience wrapper around the Places Context API and the Statistics Context API. Operations are counted in the same manner as these APIs, based on the configuration of the Location Scoring scenario.

maxRoutingTime: each 60 minutes is counted as 1 operation.

sources: each source point = 1 operation.

gravitation-based criteria: Statistics Gravitational and Poi Gravitational apply a 2x multiplier.

examples:

• Locations scores for 30 locations, with 1 Point of Interest Criteria and 1 Statistics Criteria, 15-minute walking for each = 60 Location Scoring API operations.
• Locations scores for 10 locations, with 1 Point of Interest Gravitational Criteria and 1 Statistics Gravitational Criteria, 15-minute walking for each = 40 Location Scoring API operations.

Explore how the Location Scoring API works

Deep Dive on Location Scoring

An Observable notebook by Targomo

By @targomo

December 2022