Measurability of the activity-friendly living environment in Tygron

How can the Tygron Platform help in providing insights into a sporty and activity-friendly living environment? Intern Reijn van Rooyen has delved into this subjected and looked at the possibilities. Is it possible to evaluate how a neighbourhood scores on an activity-friendly and sporty living environment? What makes an environment attractive for activity or sport and how can this be mapped in the Tygron platform?

As a result of the Webinar Data and Knowledge Hub Healthy Urban Living, where Tygron spoke, John van Echtelt asked whether it would be possible for Tygron to look into the possibilities of the healthy, sporty and activity-friendly living environment. John himself is active as a neighbourhood sports coach in the municipality of Montfoort. These events sparked me to look and research into the possibilities.

Hereby I encountered the RIVM’s key indicators of sport and exercise. The RIVM developed this method in response to a request of the Ministry of Health, Welfare and Sport to gain more insight into these subjects. Also, the ministry preferred to be able to monitor the status of these core indicators in the Netherlands and thereby be able to keep track of possible progress (RIVM, 2020). One of these key indicators is the activity-friendly living environment, in which the public space per municipality is assessed based on a five-point scale. This key indicator is developed by the Mulier insituut and described in the report Sportaccommodaties in Nederland Kaarten en kengetallen. The Mulier Institute is engaged in research of sports and physical activity in society and the living environment. The activity-friendly living environment key indicator is divided into six sub-indicators, which are highlighted in the next paragraph. The activity-friendly environment key indicator subsequently served as inspiration for the indicator of an activity-friendly and sporty living environment in Tygron.

These headings form the six sub-indicators and are also (partially) used in the indicator made in the Tygron platform.

  1. (Public) sports facilities
    For this sub-indicator, the number of available sports facilities such as sports fields or sports halls where membership is required has been considered. This is not about sports clubs, but purely about the facilities (fields/halls). This data is partly already present in Tygron and partly added by Openstreetmap. This sub-indicator looks at the level of sports facilities per municipality.
  2. Sports and play areas (number per 10,000 inhabitants)
    This sub-indicator is divided into four different types of facilities: Branded sports and play areas (such as Cruyff Courts), Unmarked sports and play areas (including playgrounds), schoolyards and marked natural living environment such as playing pond/forests. This data has largely been added from Openstreetmap.
  3. Sports, play and exercise areas (hectares per 10,000 inhabitants)
    The Sports, play and exercise areas are based on parks, public gardens and allotments. Most of the data required for this comes standard from Tygron and is supplemented with Openstreetmap.
  4. Routes (paths) (Meters per 10,000 inhabitants)
    For this sub-indicator, I have used the number of m walking and cycling paths in a municipality or a neighbourhood. Bicycle paths are only included when the bicycle is the only user on the path. This information is standard available in the Tygron platform and again supplemented with data from Openstreetmap.
  5. Countryside (Hectares per 10,000 inhabitants)
    This sub-indicator has been omitted because a distorted picture can arise when analysing individual neighbourhoods. Since urban neighbourhoods often don’t contain countryside.
  6. Proximity to amenities (Average distance per inhabitant in km)
    The last indicator and in my opinion one of the most interesting is the proximity of a neighbourhood to amenities. These facilities are divided into three categories: Commercial amenities, Public amenities and Public transport. The data of Statistics Netherlands made it possible to look into the average distance that a person must travel to an amenity per neighbourhood. Hereby the motive is that the closer a person is to an amenity, the sooner a person is inclined to move without a motorized mean of transport (SOAB Breda, 2010)

The next step was translating these sub-indicators to the Tygron Platform. With the background of John van Echtelt in Montfoort, the first exploratory research was in a part of his hometown. Hereby I looked at whether it was possible in the Tygron platform to retrieve the data needed to provide insight into these sub-indicators. For example, is it possible to retrieve the number of playgrounds for a part of Montfoort via queries or to obtain the number of meters of cycle paths? After some trial and error, this could be obtained in the indicators in the Tygron platform (see figure 1.).

Figure 1. Visualisation of sidewalks in the Tygron Platform.

Subsequently, the transition was made to an entire municipality. With the neighbourhood knowledge in mind of Statistics Netherlands, which can be found standard in the Tygron platform, I decided to carry out the analysis per neighbourhood. The choice was then made to highlight the municipality of Vlaardingen, due to the presence of differences in socio-economic situations in the neighbourhoods there. This could reveal any differences between the scores per neighbourhoods. For the municipal analysis, the score of the previously mentioned 5 sub-indicators was therefore restructured to function per neighbourhood.

In the Tygron platform, it is possible to get insight into the data of all the neighbourhoods in a municipality via one direct query. With this in mind, the translation to the Mulier Institute’s score method was made. When assessing a municipality, the institute looks at the score per sub-indicator per 10,000 inhabitants. Because neighbourhoods often have a smaller number of inhabitants, the number of facilities has been extrapolated to a situation of 10,000 inhabitants. The number of inhabitants per neighbourhoods was also easy to find with the data from Statistics Netherlands (figure 2.)(Centraal Bureau voor de Statistiek, 2020).

Figure 2. Distribution of inhabitants in the different neighbourhoods

With all the indicators completed and functioning, an interesting picture of the municipality of Vlaardingen became clear. The scores between the neighbourhoods on the various sub-indicators provided a good insight into the situation of the municipality (figure 3.). As an addition, the overlay option of the platform also makes it possible to look at other socio-economic factors in the neighbourhoods. This can be for example the percentage of social housing in a neighbourhood or the percentage of relatively high incomes. The activity-friendly living environment indicator could for example be able to support policymakers in providing insight into neighbourhoods that lag in terms of sport and activity-friendliness.

Additionally, the indicator could also be connected to environmental issues. More integrated measures could be taken into account such as the water management of a district combined with possible infrastructure renewal. As has happened in Rotterdam, for example, with city squares that can also combat flooding by storing water during peak events (Municipality of Rotterdam, 2013). Another possibility is to increase the safety in a neighbourhood to give insight in for example the sidewalks in a radius around a children’s playground and analyse if this is safe for children to use.

Figure 3. An overview of the Proximity to amenities score of Vlaardingen



Centraal Bureau voor de Statistiek. (2020, 17 juli). Kerncijfers wijken en buurten 2020.

Gemeente Rotterdam. (2013). Benthemplein |

Mulier Instituut. (2016). Sportaccommodaties in Nederland Kaarten en kengetallen. Mulier Instituut / Arko Sports Media.

RIVM. (2020). Overzicht kernindicatoren sport en bewegen. Sport en bewegen in cijfers.

SOAB Breda. (2016). Benchmark Vervoerwijzekeuze supermarktbezoekers (Nr. 23946).