Browsing by Subject "accessibility"

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.

Cellulose is a major component of plant biomass that can be used to produce bio-derived materials, chemicals, and fuels. Whereas the conversion of cellulose to chemicals and fuels is limited by the surface functionality of cellulose fibers due to their highly compact structure. A group of non-catalytic proteins including expansins, loosenins and cerato-platanins are known to alter cellulose fiber structures, presumably by breaking hydrogen bonds between cellulose microfibrils. This makes them interesting targets for the chemical and biophysical modification of plant polysaccharides. In this work, seven non-catalytic target genes were selected from the transcriptome of Phanerochaete carnosa. Pichia pastoris was transformed with expression constructs harboring the respective genes under control of the AOX1 promoter. Protein expression was analyzed by colony blot assay and small-scale expression in liquid media. Two cerato-platanins (CP1, CP4) and 3 loosenin-like proteins (LOOL7, LOOL9 and LOOL12) were successfully produced in large-scale. The identity of CP1, LOOL7, LOOL9 and LOOL12 was confirmed by MALDI-TOF MS analysis. The proteins showed no hydrolytic activity when tested on carboxymethyl cellulose, xylan or glucomannan. Defibrillation assay results of CP1, CP4, LOOL7 and LOOL9 suggested that the non-catalytic proteins lack specific weakening effect on cellulose filter paper. Considerably high protein blank absorbance of LOOL7 indicates possibility for self-assembling abilities of the protein. Incubation of filter paper with CP1 seemed to improve the hydrolytic action of cellulases on Avicel and filter paper, as shown by complementation assays. The effect of LOOLs on enzymatic cellulose hydrolysis varied over the time course of different experiments. Most notably, LOOL7 and LOOL12 were able to increase the reducing sugar release from Avicel and filter paper by cellulase at different time points. The maximum increase of cellulose conversion achieved by LOOL12 was 30% after 10 min when non-catalytic proteins and Cellic CTec2 were added simultaneously. LOOL7 showed highest conversion improvement of 21% after 6 h pre-incubation assay with filter paper. Upon prolonged pre-incubation (72 h), significant improvement of filter paper hydrolysis was noticed for CP4 and CP1, while LOOLs unable to enhance the hydrolytic activity of the tested cellulase mixture. For the first time, a comparative study of cerato-platanins and loosenin-like proteins was carried out and the findings presented in this thesis suggest that especially CP1 can be a promising accessory protein for efficient bioconversion of cellulose. Nevertheless, a deeper understanding of the structure and function of these non-catalytic proteins will be helpful in determining their potential biotechnological applications.

During the past few decades, online shopping has grown steadily and increased its share of all retail sales. As internet connections have become more common and the selection of available online services has diversified, an increasing number of consumers have started to purchase different products online. Buying online has many time-related benefits compared to traditional retail as it enables the consumer to make purchases whenever and wherever. Yet, online grocery retailing has been relatively small-scale in both Finland and abroad compared with other product categories. In the past few years, however, the competition has become more intense as the few dominant Finnish grocery retailers and several smaller businesses have developed their online business models. In this study, I focused on one of Finland's leading grocery retailers, Kesko, and the customers and spatial characteristics of its online grocery services. The aim of this study was to find out 1) whether the accessibility of services affects the choice between an online store and a physical retail outlet in the case of grocery retail, 2) whether the widely accepted socio-economic characteristics of typical online shoppers find evidence in the case of choosing online grocery retail over a physical store or the frequency of online purchases and 3) how Kesko's online grocery retail has spread in the Helsinki region during the couple of years it has been in operation and where its potential new market areas in the region are. The MetropAccess time-cost-matrix for the Helsinki Region was used for the accessibility calculations. Travel times were calculated from all inhabited cells in the area and only from Kesko's online store's customer cells to the closest Kesko grocery store and separately to the closest store when all grocery stores were taken into account in the Helsinki Region. In some previous studies, urban living environment and dense service network have been observed to increase the probability of being an online shopper whereas poor accessibility to services increases the intensity of online shopping. In other studies and national statistics data, a variety of socio-economic attributes have stood out as prominent characteristics of e-shoppers. These include: young age (age groups 25-34 and 35-39), higher education, student status and high income. In addition, I have included the percentage of underage children of a cell's inhabitants in the analyses as Kesko's own data points very clearly in the direction that families with children are an important customer group to online groceries. The socio-economic variables of the region's inhabitants were mostly drawn from HSY's SeutuCD 2015 and Tilastokeskus' Paavo zip code data. I made correlation analyses on the YKR-grid level where the other variables were 1) the percentage of online customer households proportioned to cell's population in the whole region and 2) the intensity of online shopping in customer cells proportioned to population, and the other variables were the socio-economic variables of the population and the travel-time accessibility of grocery stores. The statistically significant Spearman's correlation coefficients were not very high, but weak connections between variables could be found. Customership of the online grocery store correlates negatively with travel time accessibility and the intensity of online shopping correlates positively with accessibility, which is in line with previous findings in the literature. Of the socio-economic variables chosen for this study, the ones that correlate the most with online shopping are income (with shopping intensity) and the percentage of 25-34- year olds (with customership). Finally, I analysed some potential future areas for growth for Kesko's online grocery business in the Helsinki Region based on the previously mentioned socioeconomic variables and accessibility of grocery stores in the study area. One weakness of the study was the availability of detailed enough socio-economic data when compared to Kesko's own YKR grid-level customer data. Some of the socioeconomic variables where derived from larger spatial units such as zip codes, which weakens the reliability of the correlation analyses. However, the grid-level examination is quite coarse for the capital region as well, and especially in this case, when the customer dataset itself was quite small. The accessibility of grocery stores is relatively good in the whole study area, so the study might not bring out the impacts of accessibility of physical services to online shopping as explicitly as might really be the case if the study was carried out in an area or with product categories where physical accessibility varies more. Moreover, the study may not have sufficiently considered the special characteristics of online grocery retailing when compared with other product types. Due to the marginal status of online grocery retailing, it has not yet been studied extensively in research literature. The results, however, do partly support previous findings of the connections between specific socioeconomic variables and the accessibility of services, and the customership of online stores and the intensity of online shopping activities.

Accessibility – what can be reached from a given point in space and how – is an essential field of study to measure the physical structure of cities, travel mode choices of residents, and the competitiveness of areas. Researchers increasingly acknowledge that accessibility is a fundamental concept on understanding how urban regions work and its position in future development of cities is paramount. Travel time is considered an intuitive measure to indicate accessibility and a strong predictor of mode choice, and usually, private car is the fastest mode of transport in urban environments. A central issue which stems from private cars and accessibility is the process of searching for parking. An understudied issue, the rather stressful activity is engaged in when arriving by car at the general area of desired parking, but no space is available. Motorists are then forced to continue search for parking, significantly contributing to urban congestion. In catering to mobility rather than accessibility, the modern urban planning has made it challenging to move away from private cars toward alternative, often more sustainable, modes of transport. Travel time studies, and more specifically, parking studies, can produce accurate data to aid in this transformation. In this thesis, a parking related research survey was developed and conducted in the Helsinki Capital Region, Finland. Adhering to the door-to-door approach, the survey respondents were enquired how long it took for them to find a parking place and park their car, and walk from the car to the destination in different postal code areas of Helsinki Capital Region. To explain a hypothetical variation in parking process durations (searching for parking, and walking to one's destination) in different areas, additional questions, such as the time of the day of parking, were presented. The invitation to respond to the survey was mostly spread on the social media platform Facebook. The survey, filled out with a web application specifically programmed for this thesis, received 5200 data rows from over 1000 unique visitors. The survey results indicate that there are spatial differences in parking process durations in different postal code areas of the Helsinki Capital Region. The inner city of Helsinki was experienced as the most difficult location to park in with regional subcenters such as Matinkylä, Espoo and Tikkurila, Vantaa, receiving relatively long parking process durations. Short parking process durations were reported from scarcely built areas but more often than not these areas had extreme values reported. Interestingly, area familiarity did not necessarily translate to faster parking process, while the type of the usual parking place was a better indicator. Out of the spatial explanatory variables added in the survey data processing, zones of urban structure (yhdyskuntarakenteen vyöhykkeet) could be used to find statistically significant differences in the parking process between variable groups and study area municipalities. Making use of the Helsinki Region Travel Time Matrix, a dataset developed by the research group Digital Geography Lab of the University of Helsinki, the thesis survey data was compared to total travel chain durations. The thesis survey data indicates that the proportion of time it takes to park one's car and walk to one's destination is a much larger part of the entire travel chain than previously estimated in the dataset. The parking process times are proportionally largest in the inner city of Helsinki, where the reported parking process duration exceeds that of the actual driving segment. This thesis, its entire version history, and all of the scripts developed for it have been made available at GitHub: https://github.com/sampoves/thesis-data-analysis.

Transportation in cities is facing the challenges of congestion and environmental impact caused by the increase in traffic flows. These issues can be reduced by promoting more sustainable transport modes, such as cycling. To increase its modal share, cycling has to be an attractive and competitive choice compared to other travel modes. Digital Geography Lab in University of Helsinki has developed comparable measures for modelling accessibility with different travel modes in Helsinki region. However, cycling is missing from the data because it has been previously modelled with simplistic assumptions of constant travel speed. Little research has been carried out to assess the applicability of this assumption. The main objective of this thesis is to develop a more realistic GIS model for calculating optimal routes and travel times of cycling in Helsinki region taking into account the feasibility of the model. Other objectives are to find out what factors affect cyclists' travel speed and can the environmental factors be used as impedances in the travel time model, what kind of spatial differences the cycling speeds have, and how realistic it is to model cyclists' travel times with constant speed on a regional scale. According to previous research, among the various things affecting cycling some of the main environmental factors are slope, junctions and traffic lights. The effects of these factors to cycling speeds in Helsinki region were analysed based on individual cycling routes and on a route and segment level from the whole data with linear regression models. GPS data of cycling was collected from volunteers who had been tracking their cycling in Helsinki region with mobile sports applications. Basic background information of the cyclists was also collected to analyse the variations in speed between different background variables. Road network for cycling and walking by Helsinki Region Transport was used as the modelling network. A GIS-based map-matching method for the cycling GPS data was developed by applying a method developed for map-matching GPS data of cars. Slope was calculated for route segments using NLS 2 meter digital elevation model and the traffic light information was derived from Digiroad. Python scripts used in modelling are available on GitHub. The cycling speeds vary by cycling frequency: cyclists who stated to cycle almost every day of the week, 3-5 times a week, or a few times a week have median speeds of 24 km/h, 22 km/h and 18 km/h, respectively. Uphill slope and signalized junctions decelerate and downhill slopes accelerate cycling speeds on individual routes. Looking at the whole data, speed has a weak negative correlation between slope and different junction types. On a regional scale the effect of signalized junctions is the greatest, whereas uphill slope has the greatest effect on route-based mean speeds. The regression models do not explain the variation in cycling speeds very well (R2 ≈ 0.1) so a travel time model based on constant speeds corresponding to the different median speeds of frequent and less frequent cyclists was implemented on the network. Spatial examination shows that mean cycling speeds in parts of central Helsinki are 0.8 times slower than in rest of the area, so the cycling speeds of the model were slowed down on those segments. Slope, traffic lights and other junctions affect cycling speeds on an individual level but not on the regional scale. Based on model validation the travel times of the constant speed model correlate strongly with the real travel times of the GPS data. The model taking into account the slower parts of central Helsinki is marginally better but the difference is only slight and affecting only the routes going via the city centre. The difference in travel times caused by different constant speeds is much greater. Constant speed can hence be seen as an adequate assumption to model cyclists' travel times in Helsinki region but the personal and spatial differences in cycling speeds should be taken into account.