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  • Lehtonen, Pyry (2021)
    Geographical accessibility to sports facilities plays an important role when choosing a sports facility. The aim of my thesis is to examine geographical accessibility for sports facilities in Helsinki and Jyväskylä. The data of my study consists of the facilities of three different types of sports in Helsinki, Jyväskylä. The chosen types of facilities are ball parks, disc golf courses and fitness centers. I also use demographic data that cover the age groups of 7-12, 20-24 and 60-64. Mapple Analytics Ltd has produced geographical accessibility data covering whole of Finland which I also use as my data. In my thesis I analyzed geographical accessibility of sports facilities and compare the results to demographic data. Both the geographical accessibility data and demographic data is in 250 x 250 m grid level. the methods I used were Local Moran’s I and Bivariate Local Moran’s I. I applied the methods so that I combined the travel-time data and demographic data. The travel-times are from Mapple Insights API. The travel modes I have used are cycling and driving because people travel to sports facilities mostly by driving or by active methods, especially cycling. The travel-times to ball parks and fitness centers are overall good in both study regions. The good geographical accessibility is caused by that the service pattern is so dense for ball parks and fitness centers. The service pattern covers almost all of the inhabited area in both study regions. However, for some postal areas seem to have not so good geographical accessibility to ball parks. In some areas in Helsinki the geographical accessibility to disc golf course can be considered to be somewhat bad. For the chosen age groups only 20-24-year-olds have unsatisfactory travel-times to disc golf course either by cycling or driving. Other age groups do not show a similar pattern because of the different service pattern of ball parks and fitness centers. Demographic variables do not explain the travel times in this context. It is important to see which postal areas have good or bad geographical accessibility to sports facilities. This helps the future planning of sports facilities. In the future it is also possible to apply non spatial methods to the data I have collected or a similar dataset. It would also be possible to which demographic variable best explains travel-times. Because of Mapple Insighs API data is in 250 x 250 m grid level many applications can be developed using the data.