How many is ‘many people’? The Shopping Street

How many is ‘many people’?

The Main Shopping Street

In this entry i’ll try to figure out how many people can be expected to walk on the main shopping street in a medium sized European city.

Conclusion

Results are applicable to cities with 80 000 – 180 000 population. There are more than 200 such cities in Europe outside of the Mediterranean region. Data gathered on uneventful July days 2018-2019 during one hour between 14:00-15:00 o’clock. The below conclusions can be used to predict and compare the main shopping street demographic of a medium sized city.

Amount of people:

• About 1,50% of a city’s population can be expected to walk on its main shopping street during an hour between 14:00-15:00 (The amount varies from 1080-2550 people). 
• The amount of people decreases by about 65% after closing hours 19:00-20:00.

Gender distribution:

• The average shopping street consists of 62,5% women and 37,5% men during opening hours. The largest share of women is 72% and of men 43%. The smallest share of women is 57% and of men 28%. Hence, the share of men never goes above 50%, and the share of women never goes below 50%.
• During closing hours (roughly 19:00-20:00) the share of men and women are roughly equal at 50% each. 

Age distribution:

• 7% are between 5-14 years old
• 24% are between 15-24 years old (A relatively large part given the 9 year age interval)
• 55% are between 25-64 years old
• 14% are 64 years or older
  
  Women always outnumber men in all age groups.

Other observations:

• 15,3% of the street occupants can be predicted to carry shopping bags
• 3,4% of the street occupants can be predicted to transport themselves or others via aid, be it a stroller, walker or wheelchair.
• 2,7% of the street occupants can be predicted to eat or drink something on the go
• 1,6% of the street occupants can be predicted to walk along with their bike.
• 0,4% of street occupants can be predicted to have pets
• 0,3% of the street occupants can be predicted to carry heavier luggage
• 0,05% of the street occupants can be predicted to use some sort of rolling device, be it skateboard, kick scooter or e-scooter.
• 0,01% of the street occupants can be predicted to jog or work out.
  
• The amount of motor vehicles on the main shopping street varies significantly. While some shopping streets functions as the main commuting artery for busses and trams, others have no such traffic at all. Most vehicle movements were found in Wurzburg (67 per hour, mostly trams) and Västerås (72 per hour, mostly busses). The other 14 cities had fully pedestrianized their main shopping streets. However, none of the total 16 cities allowed for personal cars. Only in The Netherlands could you find examples of young people driving scooters on the shopping street, but generally not more than 5.
  
•  The amount of people biking varies a lot as well, depening on how integrated the shopping street is with the city, if the street width allows for biking and if there is a strong bike culture to begin with. Lund, Wurzburg, Västerås and Amersfoort had the most bikers of all observed cities, with 173, 160, 104 and 73 bikes per hour. This constituted 15%, 7%, 6% and 5% of the people who occupied each respective shopping street. The other 12 cities had well below 50 bikers per hour and generally below 2% bike share.

Observation

Many cities in Europe are aware of the changing climate of shopping. The modern society enables us to shop things in uniquely different manners, be it in the city center, the peripheral mall or via internet services. The city center is of special interest due to it being the very identity and beating heart of a city. An active shopping street is one of the most lively places a city can offer and an attractive representative to visitors and inhabitants alike. We might be able to go out in our own city and count the amount of people walking on the shopping street, but this does not give us an idea about whether this is a big or small amount of people. So, what is does the general shopping street demographic look like?

In order to answer this question I travelled to 16 European cities in order to count people on the main shopping streets, capturing data for 3 hours in each city using a GoPro Hero 5 camera, as well as making use of the application ‘Thing Counter‘ for parts of the streets that i could not capture using the camera. In this entry I’ve analyzed the time interval 14:00-15:00 for all 16 cities, and the interval 19:00-20:00 for 9 of the 16 cities. The third, excluded time interval is between 16:30-17:30. The exclusion of this is due to its similar values to that of interval 14:00-15:00 and the increased labor it takes to plot down the data.

Thanks to the work of Gehl Institute I could gather data in a standardized way by making use of the ‘Age + Gender Tally‘ and ‘People Moving Count‘ tools, with only minor adjustments. I decided to gather information on:

• Amount of People
• Gender distribution
• Age distribution
• What are people doing?

The first data group can be seen in the three graphs below. By using the data in the graphs, we can better understand the shopping street traffic volume of medium sized cities. Some cities have more than one shopping street (for example Aalborgs Bispensgade/Algade), but in the city size that I investigate, the second shopping street is usually clearly less dominant than the main one. Generally I assume the main shopping street by judging the traffic flow, looking for big retail chains as well as looking on the webpage of each city in order to find what shopping streets they themselves advertise. I also travelled to three other cities (Innsbrück, Norrköping and Enschede) which I excluded from the count. The reason is due to them having most of their shopping in central indoors shopping malls, rather than along a street.

The second demographic factor is that of gender distribution. Counting gender is quite straight forward. When talking about women and men in general terms, I include all age groups from 5 years old to 64+ years old.

In general terms regarding gender distribution; The average shopping street consists of 62,5% women and 37,5% men during opening hours. The largest share of women is 72% and of men 43%. The smallest share of women is 57% and of men 28%. Hence, the share of men never goes above 50%, and the share of women never goes below 50%. During closing hours (roughly 19:00-20:00) the share of men and women are equal at 50% each.

Örebro is a city with the clearest example of when the main shopping street (Köpmanagatan) is not also the main thoroughfare (Drottninggatan). I suspect that this makes Örebro an interesting case study for shopping streets as well as for main thoroughfares. However, Würzburg is one of the most obvious examples of when the main shopping street and the main thoroughfare are the same street (Schönbornstraße), and yet it has a similar gender distribution to that of Örebro. For an easy comparison, look at the two rightmost bars in the graph below. Based on this data, it appears that the demographics of the busiest streets are made up of mostly women. This changes however when stores are closing.

The third data group is that of age distribution. Firstly, it’s clear by the data that women/girls are always more than men/boys, regardless of age group. However, the least disparity can be found in the youngest and the oldest age groups. The oldest and the youngest age groups are also the groups that have the least representation, with a share of 14% and 7% respectively. The largest age group is that of people between 26-64 years of age, that have a total share of around 55% of the total street demographic. This is unsurprising since it’s also the age group that covers most people in general. The second largest group is that of 16-24 years of age, having a total share of 24% of the street demographic. Given that this age group only spans about 10 years, suggests that it is  especially active on our main shopping streets. It is however worthy to note how difficult it can be to spot the difference between a 24 year old person and a 30 year old person.

The age and gender distribution varies from city to city. It’s interesting to note how some cities have a higher degree of older people than other cities. Maybe it comes down to pure chance? However, based on my own observations and somewhat backed up by my data, I would say that the more preserved or picturesque city centers like Deventer, Helsingborg, Koblenz or Lund are likely to have more older people that are seemingly touristing. Deventer has a share of older people that is about 22%, Helsingborg about 21%, Koblenz about 20% and Lund about 18%. All other cities had values lower than 15%. Aalborg and Osnabrück had the least share of older people at 10% and 9% respectively. While both Osnabrück and Aalborg do have picturesque city districts, and Osnabrück in particular, the main shopping street is not situated therein. However, this analysis could be a bit of a stretch and I have not cross-referenced it with other tourist data. Würzburg for example is a very picturesque city, but its share of older people are at the median of 15%.  However, Both Lund and Würzburg are famous student cities which might be a cause for the share of older people to go down.

Örebro, Osnabrück and Würzburg have the highest share of young people at the age of 15-24, with 30%, 29% and 27% respectively – as compared to the median of 25%. Örebro is the city with the most pronounced shopping street, being separated from the city’s main thoroughfare. And Würzburg is one of the cities where the main thoroughfare and the main shopping streets are clearly the same. These three cities also happens to have the biggest disparity between men and women in this age group. They have a 16, 10 and 9 percentage units difference respectively, with women being more than the men. The average disparity between gender in this age group is 8 percentage units. The city with the least disparity in this age group is that of Koblenz with the anomaly of 0.9 percentage unit difference – where females just barely outnumbers men. Putting Koblenz 0.9 disparity next to Örebro 16 disparity is surely intriguing and something worth delving deeper into – but that would be for another time.

The disparity of gender in the age group of 25-64 year olds are more regular across all cities and is averaged at about 15 percentage units. There is no particular city that deviates too far from this number and I would have to conclude that this age group is quite reliable in terms of gender disparity. The same conclusion can be done for the youngest age group of 5-14 year olds, with an average disparity of merely 1 percentage unit. Hence, with such a low average disparity, the youngest age group is also the most equal age group in terms of gender distribution.

The last data group is concerned with what people are doing who travel on the main shopping street. This data group has 9 different factors. Those components as well as the data summary can be seen in the right graph above. Each factor is clarified and commented below:

1. Shopping bags:

A person that carries at least one shopping bag. This could be an indication of how active the street demographic is at making puchases. It’s also an indication of how much exposure a shopping bag and its associated brand can get in a busy area of the city.

The median percentage of shopping street occupants carrying shopping bags is 15.3%. Only three cities deviates from this in a significant way. In Würzburg and Ingolstadt only 10.5% and 11% carried a shopping bag. The other deviation was that of  Koblenz were a significant share of 26% of the street occupants carried shopping bags. I have not been able to find a reason for this high percentage value. There were no events in the city and the weather was cloudy. Surprisingly, Örebro, with one of the most pronounced shopping streets did not deviate from the median value much with its 14.3% share of shopping bag carriers. The other 13 cities were all within a 2% range of the median 15%, which makes the median value a reliable value for predicting the amount of people carrying shopping bags.

2. Carried:

A person that is in a stroller, wheelchair or any other form of aid in order to transport themselves. This might give an indication on how many people stroll along the main shopping street despite transportation difficulties.

The median share of people being transported with aid is 3.4%. This value has a strong prediction value with most cities aligning close to this 3% value. Only Örebro deviated significantly with a share of 6.3%. Västerås and Würzburg are the two cities with a relatively large amount of bus and tram traffic on their shopping streets. Their main shopping streets were also among the widest of all 16 streets. The presence of large motor vehicles did not seem to impact the share of carriers. Västerås has a share of 3,2% and Würzburg has a share of 2%.

Gehl Institute separates between strollers and wheelchairs/aid. For practical reasons I had to limit the amount of components, and these two groups became one, namely ‘Carried’. In hindsight, I should have done it differently. The value does still give insight into the amount of people getting using aid on the main shopping street.

3. Eat & drink:

A person eating or drinking something on the go (not counting personal/workout water bottles). This indicates how common it is for people to consume (and perhaps purchase?) food products along the main shopping street.

The median amount of people eating and drinking on the main shopping street is 2.7%. To my surprise this value have a high prediction value as well. My assumption was that cities with more take-away shops along their shopping streets would have more people eating and drinking along it. Especially the Dutch and Danish shopping streets offered a more wide array of such shops – mostly ice cream and cold juice drinks. This was not exactly the case. Although cities with less such shops such as Jönköping and Västerås did have a lower value than the other 14 cities. Jönköping had a share of  1.2% and Västerås a share of 1.7% – maybe a missed business opportunity? Only one city really out shined the others when it came to people eating and drinking along its shopping street, and this was the city of Odense with a share of 5.1%- almost double that of the median value. This was also the general feeling I had when I spent time in Odense as the athmosphere was very leisurely and invited to unhurried activites. 

4. Leading bikes:

A person that is leading their bike, as opposed to actually riding it. 

This value has somewhat of a predictive value but it is likely very influenced by the street width, the perceived allowed pace as well as the general bike culture of the city. The medium share of bike leaders is 1.6%. The highest share is 9.5% in Amersfoort, and the lowest is 0.3% in Västerås. Deventer also have a high share of bike leaders at 5.6%. Thus, the Dutch cities might be expected to experience a much larger share of people leading their bikes as compared to the other examined cities. All other cities have a share lower than 3.5%, and most below 2%.

5. Bike riders:

A person who is actually riding their bike.

This cannot be perceived to have much reliable predictive value at all. The values ranges too much and too many factors are involved in order to make a reliable prediction with only the data that i’ve gathered. Street width, bike culture, street integration, perceived pace and traffic rules all play a significant part in this sort of prediction. The median share is 1.6%, the highest share is 15.4% in Lund and the lowest share is 0% in Aalborg.

Lund, Wurzburg, Västerås and Amersfoort had the most bikers of all observed cities, with 173, 160, 104 and 73 bikes per hour. This constituted 15%, 7%, 6% and 5% of the people who occupied each respective shopping street. The other 12 cities had well below 50 bikers per hour and generally below 2% bike share.

6. Pets:

A pet of any sort. In reality, almost exclusively dogs.

We’re heading into the very low percentage values with this factor. The median share of people with pets on the main shopping street is 0.4%. The highest share of pet owners were in Bolzano with a share of 1.3%. However, it’s  generally safe to say that the amount of people walking with pets is less that 1%.

7. Luggage:

A person with a heavy traveling luggage.

The luggage statistics have a similar conclusion to that of the above pet owners. The median value is 0.3% and is mostly below 1%. Only Helsingborg surpasses this in any significant way with a share of 2.2% of the people carrying luggage.

8. Rollers:

A person who transports themselves using scooters, e-scooters, skateboards or any other form of small wheeled transportation. ‘Rollers’ use the devices either for fun or for increased speed, as opposed to ‘Carried’ who do it out of necessity.

The median value is 0.05%. About 7 cities had no rollers at all. It’s rather safe to predict that the value does not go above 0.3%.

9. Workout:

A person who is jogging or otherwise working out. 

Very few cities had any people working out during this time of the day on the shopping street. The value is likely not to go above 0.05% unless there is an organized workout event.

WÜRZBURG