Choices of Destinations

How to characterise
destination choice behaviour

Places visited in a two year period by one person

We love that in a world of digital realities, 'real' people give 'real' places a 'real' meaning. But how do people select their destinations to make those places 'real'? What kind of activities are conducted there? Do the frequencies reflect routines? And what role does the mode choice play? Just a simple look on descripive measures can help understand destination behaviour a little more. Especially, since smartphone tracking allows for some nice long term data providing visted places and some time the purpose of the visit.


The authors also had a look at their own personal data and were to some degree surprised...

Berlin, Aug. 2024. Authors: Divya Crasta, Benno Bock



The individual view: three examples of destination choice behaviour

The phenomena can be looked at a collective level as we will discuss further below. But let us make it graspable with three examples on the individual level first. Three people close to Catchment provided their Google Location History data for two full years. That is (almost) every trip and every activity each day each week - as long as they had their smartphones with them and switched on. A powerful set of data in terms of a) movements and b) stops. Stops provided information on the places visited, their addresses and giving an estimate on the according activities been conducted there.

Divya

Divya
  • activities: 2.6 per day
  • activity area: 18 km²
  • low frequency places: 14.8%
  • activity types*:

Benno

Benno
  • activities: 3.42 per day
  • activity area: 145 km²
  • low frequency places: 23.0%
  • activity types*:

Wiebke

Wiebke
  • activities: 4.47 per day
  • activity area: 49 km²
  • low frequency places: 23.6%
  • activity types*:

*) without 'home' and 'waiting'

Destinations: a place, a visit and an activity.

At this point it makes sense to make a major distinction: the one between places, visits and activities. Above, we have already used both terms. A place is the premise at a geolocation, the visit is the a stay at the place, an activity is the purpose of that visit. In the map we illustrate the physical places colored per activity type. In the pie charts, we use the visits colored the activity type. Every visit can more or less be attributed to a place with coordinates. The visits per place for a certain time discribe the frequency that place was visited. The activity type as the purpose of the visit and therefore also the prior trip adds another dimension. Without actual verification by observed individuals, it is rather hard to derive clear indication on the activity when using smartphone tracking data. Therefore, the biggest challenge when handling tracking data is the allocation of activity types to places and visits.

Looking at these attributes, we find another thing that was surprising to us: the vast "rat tail" of destinations. Those are all the places that have only been visited once or twice a year. The majority of places actually belong to this long rat tail with very low visiting frequencies. Only a small minority of places have more visits. They make up the majority of visits, seemingly a contradiction to the last sentence but simply a result of the unequal distribution. In overall numbers, for our data guinea pigs, the typical regular places 'home', 'workplace' and 'educational institutse' are the most dominant ones in terms of visits, but in the end could only be three or two places - or in the case of home offices just one. The usual supermarket for your daily needs, sport facilities or kindergarden for drop-off and pick-up of kids can be other high frequency places. But there seems to be large differences of this phenomenon according to the type of activity of visits and from what we can tell from our small sample on individual level.

To the right we find an overview of places and visits by activities from one of the three individuals. The distribution is displayed in a Lorenz-Curve. Activities with a /-shaped distribution have destinations with an equal frequency between low and high frequency places. Activities with a J-shaped distribution have places with an unequal distribution. In those cases, only a few places make up most of the total number of visits of that activity type. The exemplary data show that activities like accompaniment, retail for daily needs and leisure activities seem bound to a few places. At the same time, /-shaped distributions such as food services, errands and retail for non-daily needs spread accross a lot of places which hint towards volatile destination choice. A similar curve can be seen for work-related visits.

Few places, many visits: the Lorenz-Curve of visits

X-Axis: cummulative share of places by visit frequency

Y-Axis: cummulative share of visits

For Catchment's Connectivity Platform, activities with a /-shaped distribution are the better fitting use-cases as they show that people tend to be more flexible in the choice making of their destinations.

The collective view

As the data sources above show, large sets of activity data can be surveyed - especially over a long period of time. Such data help to understand a little better how activities, places, visits and visit frequencies behave not only for a large survey group but also for different "user" types.

Activity types

The publicly available data from the largest German mobility survey MiD 2017 provide insights on the distribution of activity types, but lacks information on places and their visit frequencies. For Germany, this data provides probably the best information on activity types. The distribution over activity types is split up by leisure activites, work and educational activities as well as errands and retail activities. The MiD aggregates the survey data in this step in different ways. For our activity analysis, we applied the 'MiD 2008' procedure. Here, the last trip before arriving at home is attributed to the last activity. The new formular for 2017, the last trip is attributed to the 'main' activity during the out-of-home period. This means in comparison to the tracking data there will be different outcomes accordingly.

Weekly innovation rate of destinations

The innovation rate indicates trips with new or unusual destinations for the surveyed person - this time independently of activity type. Interestingly, during COVID-19 data analysis showed that this made for a period of new orientation as the innovation rate peaked for a longer period during the first lock down in the 1st and 2nd quarter of 2020. Prior to and after covid, innovation rates were rather similar. The values have been derived by ETH Zürich. The innovation rate for destinations lies approximately at 16% of all trips. For this share of trips, we assume high openness to nudging.

Key take outs

Looking at activities, places and visits is a great way to put our daily decision making into perspective. How carefull one has to be regarding the generalisation can be seen by the KPIs of the three examples above.

There is a lot to learn about individual destination choice behavior. Deeper knowledge on this can be helpful in:

  • spatial planning and design
  • improving transport models
  • geomarketing and location descisions

Above, we also raised the question what kind of role the transport mode plays in decision making for activities and destination choices. This makes for yet another blogpost.

Attachments

The MultiMoFusion-Project

Our research project MultiMoFusion has been analysing smartphone tracking data with the aim of merging individual data points - from floating phone data (FPD) on the one hand and from smartphone tracking data (SMASI - Smartphone Assisted Self-Interview) on the other. MultiMoFusion was kicked off in November and will run for three years. The consortium is being funded as part of the FFG call for proposals "Mobilität (2022) - Städte und Digitalisierung". Catchment is supporting its partners TU Wien, TU Graz, Invenium and Entwicklungsgesellschaft Wien 3420.

A tree-view of activities

  • MiD 2017
    • home
    • work
    • work-related
    • education
    • retail
      • 'daily needs'
        'other goods'
        'general shopping'
        'services'
        'other'
    • errands
      • 'services'
        'visit to the doctor, other medical services'
        'public authority, bank, post office, cash machine'
        'private errand for another person (free of charge)'
        'caring for family members, acquaintances'
        'visiting/meeting friends, relatives, acquaintances'
        'further education'
        'restaurant, pub, lunch etc.'
        'walking the dog'
        'church, cemetery'
        'volunteering, club, political activities'
        'working in your free time for a fee'
        'accompanying children'
        'hobby'
        'other'
    • leisure
      • 'general shopping trip'
        'private errand for another person (free of charge)'
        'caring for family members, acquaintances'
        'visiting/meeting friends, relatives, acquaintances'
        'visit to a cultural institution'
        'attending an event'
        'sport (active), sports club'
        'further education'
        'restaurant, pub, pub, disco'
        'allotment garden, weekend home'
        'day trip, short trip up to three nights'
        'holiday (four nights or more)'
        'walk, cruising'
        'walking the dog'
        'jogging, inline skating etc.'
        'church, cemetery'
        'volunteering, club, political activities'
        'accompanying children'
        'hobby'
        'playground, playing in the street, etc.'
        'other'
    • companion

  • MotionTag smartphone tracking
    • at home
    • working
    • education
    • family & friends
    • shopping
    • errands
    • eat out
    • leisure
    • sports
    • medical visit
    • drop off / pick up someone
    • waiting
    • other

  • Google Location History
    • work
      • 'office'
        'city_hall'
        'courthouse'
        'embassy'
        'local_government_ office'
    • work_related
      • 'airport'
        'train_station'
        'subway_station'
        'light_rail_station'
        'transit_station'
        'travel_agency'
    • education
      • 'school'
        'university'
        'library'
        'primary_school'
    • retail
      • 'bakery'
        'clothing_store'
        'convenience_store'
        'electronics_store'
        'florist'
        'grocery_or_ supermarket'
        'hardware_store'
        'home_goods_store'
        'shopping_mall'
        'store'
        'supermarket'
    • errands
      • 'atm'
        'bank'
        'car_repair'
        'car_wash'
        'dentist'
        'doctor'
        'drugstore'
        'finance'
        'gas_station'
        'hair_care'
        'hospital'
        'insurance_agency'
        'laundry'
        'lawyer'
        'pharmacy'
        'physiotherapist'
        'plumber'
        'post_office'
        'real_estate_agency'
    • leisure
      • 'amusement_park'
        'bar'
        'book_store'
        'bowling_alley'
        'cafe'
        'campground'
        'movie_theater'
        'museum'
        'natural_feature'
        'night_club'
        'park'
        'stadium'
        'tourist_attraction'
        'zoo'
    • companion
      • 'cemetery'
        'church'
        'funeral_home'
        'health'
        'lodging'
        'meal_delivery'
        'meal_takeaway'
        'restaurant'