6. Discussion
6.5 Future considerations
There is work to be done in the future. It would be possible to add an imaginary sports facility in the data and use the study design workflow to produce travel time maps and Local Moran’s cluster maps with the imaginary facility included. This imaginary sports facility could be placed somewhere where a facility is being planned. This could show how the new facility changes the Local Moran’s I clusters. Also, by looking at the Local Moran’s I maps, we can see where the new facility could be placed. It is the combination of these two, knowledge about the current situation and the possible future that enables some analyses of the Local Moran’s I maps. Of course, it requires more knowledge than just the LISA clusters to decide where a new sports facility needs to be built.
Furthermore, to plan any sports facility the facility requires space. Especially disc golf courses require much more space than a football park or a fitness center. As the space requirements and policies affect the planning very much, it is difficult to solve the unequal opportunities for disc golf or other sport that requires space. For example, the center of Helsinki will probably stay as a HH area in the LISA cluster map even though a new disc golf course would be built somewhere
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in Helsinki. This is because there is most likely not enough space for a disc golf course in the city center.
In the future using Mapple Insights API it would probably be possible to download accessibility data from a sports facility type that has very few facilities per municipality. The downloading would mean that for every facility in Finland the travel time data would be downloaded These facilities could be disc golf courses, public swimming pools or even padel fields. The downloading would take a lot of time but it would be needed to be done only once.
There is a possibility for more advanced analyses with travel time data in relation to demographic data. For example, geographically weighted regression (GWR) would be possible to make using the travel times as the explanatory variable. However, housing policies in Finland might affect the results meaning that it is difficult to create a GWR as there are not many large areas that are segregated in Finland. Of course, segregation exists but it exists in such small amounts that GWR cannot account these places. But some other variables that are not related to housing policies can be used in GWR. On top of GWR, non-spatial methods could be used to further analyze the travel times and demographic variables. For example, Ordinary Least Squares model (OLS) and Lagrange Multiplier error could have been used. But it was for the large amount of different datasets why the analyses were not done in this thesis.
I have used the fastest travel time when I have calculated the travel time matrices for sports facilities. However, for example Londoño (2020) service areas for multiple facilities method uses the median travel time for facilities. This can be considered to be a more realistic scenario, as it accounts the fact that people do not only go to their closest facility. The code I have created can be changed to match Londoño’s (2020) work but for this thesis only the travel time to nearest facility is used.
The travel time data for the three types of sports facilities was compared to only three types of age groups. This restricts interpreting the results. In the future especially football parks could be compared to younger age groups, such as children aged 3-6. Disc golf and fitness centers could be compared to other age groups, as well. Analyses currently lack data of middle-aged people and teenagers. Using multiple age groups and other demographic variables than median income could be considered in the future.
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The comparison of age groups was discussed in Mäntyniemi’s (2015) thesis. Also, she talked about using genders as the other variables, too. This is in line with Dobbs (2005) article, women might be less mobile than men. But there is no guarantee whether women have worse accessibility to facilities than men. By using genders as a variable in further studies we could conclude if women and men have unequal accessibility to sports facilities. This is however improbable at least in Finland where housing policies affect the accessibility.
To further analyze the travel times, it will be possible to compare Mapple Insights API’s travel time data to an open-source alternative, Lipas service. In Lipas there is an accessibility tool being developed. The tool is different from Mapple Insights API in many ways but it produces accessibility measures similar to Mapple. The difference of travel times between OSRM routing and Mapples routing would be interesting to see.
45 7. Conclusions
While accessibility of sports facilities is not a topic that is researched much (Kotavaara & Rusanen, 2016), there seems to be many ways to analyze them using GIS. This study included only three different sports facility types but still revealed patterns in travel times that would probably not have been able to be seen otherwise. It was the objective of my thesis to bring new methods to analyze accessibility of sports facilities. When using exploratory GIS techniques it was possible to provide new insights into analysis of sports facilities.
The data and methods are suitable to analyze the travel times of sports facilities in Helsinki and Jyväskylä. There seems to be spatial clustering with the data that can be seen from the Moran’s I maps and statistics. Exploratory techniques seem to be well-suited for analyzing travel times for sports facilities. Looking at the results we can see that some areas in Helsinki and in Jyväskylä are more deprived of sports facilities than others. This can be seen from the number of HH clusters and LL clusters in postal areas.
I have used the proprietary Mapple Insights API with openly available software Geoda and QGIS.
With Mapple Insights API it was possible to retrieve information about travel times that wouldn’t be otherwise possible. Combining data from Mapple Insights API to YKR grid proved to be crucial in terms of results of my thesis.
Although it was important that Mapple Insights API data can be joined with the YKR grid, it required manual configurations to the data. Jupyter Notebook was not able to load the full YKR grid of Finland and was therefore needed to be split for municipalities. This is an extra step that takes time and the full analysis made in this thesis cannot be considered to be fast. Also, creating queries for Mapple Insights API, doing the analyses using Geoda and making the final visualizations with QGIS requires more manual work. The results are actually behind a lot of manual work. It is difficult to assess the exact time that is taken in this process because it is very dependent on how many sports facilities there are to be analyzed.
An analysis that accounts only free to use sports facilities requires that Lipas has up to date information about vapaa_kaytto field. I was not able to take full advantage of this attribute information. If this field was updated for some facilities in one municipality, it could provide research opportunities for the municipality and researchers working for universities.
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While my thesis has covered the aspects of accessibility in travel times, accessibility is a broad term that includes many layers. These layers include social accessibility, cultural accessibility and accessibility of information (Virmasalo, 2021). In the future it would be interesting to see some of these accessibilities being accounted in studies. Self-efficacy could be one aspect that a more qualitative study could include better. Furthermore, a more qualitative study could include the personal preferences of people when choosing a sports facility.
As there has been mixed findings about availability of sports facilities compared to socioeconomics (Higgs et al., 2015), the results remain inconclusive regarding this. There seems to be spatial clustering of many sorts. Some are more alarming signals than others. It can be assumed that the density of sports facilities indeed increases the usage of sports facilities (Sallis et al., 1990). While Mapple Insights provides travel time data for any facility, it seems that analyzing travel times to sports facilities requires local knowledge of the areas where the travel times are.
My main finding was that there are clusters in travel times. But not all of the travel times disperse to every direction. This suggests that the road networks and the locations of the sports facilities are not equally located for people in Helsinki and Jyväskylä. These results can be best seen with disc golf courses that are much more sparsely built than football parks and fitness centers. Using sports facilities that have fewer locations reveals patterns in accessibility better than sports facilities that have a very dense service pattern. In the future there could be a more thorough analysis of a single sports type that could reveal even more patterns in accessibility.
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