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Browsing by study line "Geoinformatiikka"

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  • Haurinen, Hanna (2023)
    Tropical montane forests are important environmental factors globally as they preserve biodiversity, carbon, and moisture. That is why it is necessary to have knowledge on the distribution and condition of these forests. Acknowledging what kind of changes happen and in what timescale, assist in forest management and planning. Remote sensing based change detection is one of the ways of investigating these changes. In this study change detection is conducted with multitemporal airborne laser scanning (ALS) data from the years 2014/2015 and 2022. ALS produces three-dimensional point data which can be further processed into different elevation models and canopy height model (CHM). This study focuses on observing changes in tropical montane forests in the Taita Hills, in Kenya. The study area included two peaks, Yale and Ngangao, which are part of the Eastern Arc Mountains, which is a bigger mountain chain stretching from Tanzania to Kenya. Both forest areas include native montane and exotic vegetation. Both positive and negative canopy height changes (i.e., tree height growth and tree loss) between these different forest segments were studied. The forest segmentation used in this study is based on earlier mapping in the area, but it was updated with the help of field observations, orthophotos acquired in January 2022 and CHM. In addition, the effects of point density on canopy height metrics were studied. The results indicate that overall trend in the forests has been positive height change. The canopy height changes show that tree growth in the forests differ between the forest segments. Areas with exotic tree species grow faster than the areas with native montane vegetation. When comparing the different tree species, eucalyptus seems to grow fastest, followed by pine and then cypress. Furthermore, some spatial differences were also noted, as similar forest types had grown more in the southern parts of Ngangao forest. Also, negative changes were observed in the data and treefall gaps were identified from the CHM. Results indicate that there is no distinctive pattern between the different forest segments and tree species in treefall. Falling of a tree can be a result of many things. It can be done purposely, or it may happen due to natural causes. Inspections of the effects of point density proved that the attributes do affect canopy height metrics derived from the data. Higher point density differences between the data resulted in larger difference values in the canopy height difference models. In accordance with other studies, it could then be concluded that high point densities create overestimations and lower point densities create underestimations of vegetation heights. Point density differences are one of the issues that should be considered when working with multitemporal data. In addition, variations in the data acquisition create uncertainties to accurate comparison between the data. This thesis provides valuable information about the changes happening in tropical montane forests in the Taita Hills. As the results demonstrate that different forest types have different growth speeds, the information can be further applied in practises that recognise forest type segments. This is crucial in determining montane forest segments. These results are suitable for further analysis and research. When considering the environmental effects tropical montane forests have and how their changes effect the local and global climate, it is useful to know how different species grow and survive in different environments. As the results show, eucalyptus seems to thrive in the area but the effects of exotic species to the biodiversity should be noted as well. In the case of eucalyptus, it uses a lot of water resources to grow, and the undergrowth might not be as rich as in native montane tree species areas.
  • Laaksonen, Iivari (2022)
    Multi-local living is a complex social phenomenon that is tightly connected to human mobility. In previous research, the phenomenon has been mainly researched with official statistics that fail to capture the dynamic nature of people’s mobilities and dwelling. This thesis approaches multi-locality in Finland and in the county South Savo from the perspective of second homes with novel data sources like mobile phone data and electricity consumption data. These spatially and temporally accurate big data sources can be used to ensure sufficient coverage of population and geographic area. I approach multi-local living by analyzing the spatiotemporal changes in people’s presence with mobile phone data, and by examining how the changes relate to second homes in different areas separately for workdays and weekends. This is examined both for the whole country and by comparing different counties. In the thesis, mobile phone data is utilized as the ground truth to assess the performance of household occupancy detection methods for electricity consumption, and to examine how electricity consumption data captures the spatiotemporal dynamics of second home users in South Savo. The results indicate that people are generally more mobile during the summer, and the seasonal growth in people’s presence correlates strongly with second homes. This shows a prominent seasonal effect for multi-local living in Finland. Additionally, it is shown that the results vary spatially as there is variation in the results both between counties and within South Savo. The best performing second home occupancy detection method is revealed by correlation analyses between mobile phone data and electricity consumption data. Moreover, it is shown that electricity data correlates better with mobile phone data during the summer, and that the data captures the monthly dynamics of second home users well. This further highlights the seasonal effect of multi-local living. The thesis provides valuable insight into how the seasonal variation of population in different areas is connected to multi-local living in Finland. Furthermore, it is shown that novel data sources can capture the changes in people’s presence at multiple spatial levels with high temporal accuracy, and that they can be utilized to study multi-local living.
  • Krötzl, Julius (2019)
    During the last decades, Helsinki and many other cities have begun to restrict parking supply in the city center and in transit-oriented developments, in order to minimize the negative impacts of parking and to restrain growth in housing prices. However, residential parking supply should only be reduced in areas that are well served by public transportation. In last years, novel data sources have been created to simulate the transportation network and land-use distribution in the future. By using computer-processing capacity to combine the travel time and land-use data sources, potential accessibility in the future can be modelled. The aim of this thesis is to provide information on future accessibility by sustainable travel modes, by taking into account the different distance friction characteristics of different land-use opportunities and to estimate car ownership in Helsinki in the year 2030. This thesis has been done as an assignment for the traffic and street planning unit of the City of Helsinki. Methods of this work include distance-based potential accessibility measures, which were computed by combining travel time matrices and land-use data using Python scripts and a geographic information system (GIS). In this work, travel time was used as the transport element of accessibility. For choosing the distance decay functions for the accessibility measures in this thesis, empirical travel data from the Helsinki region travel survey was used, which consists of travel times and trip purposes of the residents’ daily journeys in the Helsinki region in 2012. Travel time and land use estimations for the years 2017 and 2030 from the Helsinki region traffic forecasting system (HELMET) as well as geographic information data from the SeutuCD registers were used as input data for the accessibility analyses. In addition, factors affecting car ownership in the Helsinki region were analyzed and linear regression models were created to estimate future parking demand in Helsinki using accessibility and population density variables. According to the results, potential accessibility measures model the mobility patterns more realistically than cumulative opportunity measures as they weight each feature according to the distance from the origin zone. By comparing potential accessibility results by different means of transport, it can be stated that sustainable transport accessibility in 2030 is, compared to the car still very low. According to the car ownership correlation analysis, the independent variable with the highest correlation coefficient is the percentage of gross floor area of blocks of flats of the entire gross floor area of residential buildings in the zone. The independent accessibility variable with the highest correlation coefficient is the percentage of potential job accessibility by public transport in relation to car, which has a strong negative effect on car ownership (R ≈ -0.8). The highest R-squared value of the multiple linear regression models predicting car ownership is 0.66, meaning that 66 percent of the variation of car ownership can be explained by the independent variables. Thus, the predicting model can be used in estimating future car ownership, if the relationships between car ownership and the predictive variables are assumed to be constant over time.
  • Heittola, Suvi (2021)
    High-quality address data is an essential part of a functioning society and its services. However, shortcomings have been identified in the quality of national address data that can, at worst, slow access to vital help in an emergency. Partly for this reason, National Land Survey of Finland (NLS) is developing a new national address information system (OTJ), which in the coming years will serve as the main database for Finland’s national address data. The OTJ's quality management methods are still under development. Currently, the incoming address data of the OTJ is planned to pass through a quality control service called Laatuvahti, which takes care of logical consistency of the incoming data by using quality rules. Preliminary quality rules of address data have been designed for the Laatuvahti service. However, the adequacy of the quality rules and the functionalities of Laatuvahti service to the quality management of address data has not yet been studied extensively. It has also not been clarified how well the quality management methods fit the needs of the users of the address data. In this master’s thesis the quality needs of significant address data users are discovered, the suitability of the OTJ's current quality management methods to the quality needs are examined, and it is determined how the quality management methods should be developed in the future. In addition, the quality needs are used in determining what does quality in address data mean. The address data users’ experiences on the quality of the address data were investigated through expert interviews. A total of seven interviews were conducted. The interviewees were selected to represent socially significant users of address data that use the data for different purposes. Interviewees were the Emergency Response Center Agency, the safety and rescue authorities, a navigation company, a telecommunications company, an energy company, a transport company and the Statistics Finland. The suitability of the OTJ's quality management methods was assessed by comparing the users’ quality needs with the existing address data quality rules and the functionalities and possibilities of Laatuvahti service. The suitability of Laatuvahti for quality needs was further verified by a service expert (from NLS). Most of the quality needs that the address data users raised in the interviews were related to thematic correctness of the address data (i.e. the correctness of the address name and number), positional accuracy and ensuring completeness and currency of the data. In addition, some of the needs were related to the address data structure, uniqueness and methods of reporting the quality level of address data. Based on the quality needs, the quality of address data can be defined simply to mean that the address data points and directs accurately to the intended location based on both its location information and the address name and number spelling. The results suggest that the OTJ's quality rules and the functionalities of Laatuvahti service only partially meets the needs of the users. The quality management methods are not suitable enough for managing the completeness and currency of the data. Some good efforts had been made to ensure thematic correctness through the quality rules, but the methods could be developed further. Positional accuracy was poorly ensured by the quality rules, but the methods could be developed to ensure the accuracy of location information better in relation to the user needs. In addition, the uniqueness of the address could also be ensured in a more versatile way. According to the results, new quality checks should be developed for the OTJ's quality management to ensure, among other things, the positional accuracy and the uniqueness of the address. In addition, recommendations for the structure and content of address names and numbers should be clarified and the quality of reference datasets used in the quality control should be ensured. In the future, it should also be clarified how the completeness and currency of address data can be monitored and should the quality results be reported in a feature level with some sort of a quality indicator value.
  • Järvinen, Matias (2022)
    Retail location analysis has been a widely researched topic during the last decades, and models able to estimate consumer and purchasing power flows are valuable to retailers and investors. As the retail market is getting increasingly competitive and consumer habits are changing, the demand for sophisticated modeling techniques remains high. Spatial interaction models (SIMs) have been proven effective for simulating consumer behavior and estimating store revenues in a high degree of accuracy. This master’s thesis examines the suitability of spatial interaction models for simulating the grocery market in Helsinki metropolitan area (HMA). Three iteratively calibrated SIMs are developed, and generated revenue estimates are compared to each other and actual revenue figures of HMA grocery stores. Based on the modeling results, factors preventing more accurate results are identified and suggestions are given for future model development. The study focuses on year 2019, and main datasets used are Nielsen grocery store register and Statistics Finland’s Grid Database. Travel times between supply and demand zones of the model are based on Helsinki Travel Time Matrix 2018 by Digital Geography Lab. The developed models can forecast revenues of the 323 studied stores quite accurately, and in the best case over half of the revenues are forecasted within a 25% error margin. A high coefficient of determination is achieved even with a simple model, and the disaggregated versions further improve the results. The models estimate the revenues of large hypermarkets the best, while there is more variance in the estimates for smaller stores. The results indicate that a spatial interaction model suits well for modeling the grocery market in the study area. The lack of empirical consumer flow data did not prevent the calibration of the model, and passable results were achieved with the iterative calibration approach. However, the developed models remain theoretical due to the lack of empirical datas. This, in addition to other results of the study, underline the importance of empirical calibration data when developing robust modeling solutions. If suitable empirical data was available, combining it with highly granular demographic data such as Grid Database, might enable very accurate modeling of consumer flows. Models able to consider current changes in the grocery sector, such as e-commerce and the refurbishments, could be valuable to both scholars and commercial operators in the retail sector.
  • Kujala, Sanna (2021)
    This master’s thesis examines the location privacy perceptions, privacy behaviours, and uses of location online. Young adults have grown up in the era of the internet, which means that they have big data collected of them since their first day online as teenagers or younger children. The topic of perceived location privacy is yet to be researched in Finland. The aim of this thesis was to understand; what are the location privacy perceptions and location privacy knowledge of young Finnish adults, and to see if the privacy paradox is in effect within the young Finnish adults. The research is based on a qualitative method and three focus groups were held to collect the data. During focus groups sessions the participants were free to discuss location privacy-related topics and their own experiences. From there on the data was analysed by using inductive content analysis methods on Atlas.ti program. The results of this research indicate that young adults are not confident in their knowledge of location privacy. The participants voiced clear worry over their privacy while using social media and mobile phones, but actions towards protecting personal privacy were not taken. This disparity between privacy concerns and taken privacy protection action was identified as privacy paradox. Most commonly, the individual’s privacy concerns were towards unknown individuals rather than the platforms or companies behind them. Aside from discussing their privacy concerns, the young adults voiced several instances of beneficial usage of personal location sharing, such instances were: location sharing to friends when trying to find each other in public places, personal navigation, quantified self, and others. It was found that young Finnish adults found it concerning that they have a lack of interest in their privacy, but still stated that they might not work to improve their knowledge or measures taken to protect their privacy online. The aspect of geographical location did matter to the young adults, and Finland and Europe were seen as most privacy protective countries of origins for applications and services.
  • Helle, Joose (2020)
    It is likely that journey-time exposure to pollutants limit the positive health effects of active transport modes (e.g. walking and cycling). One of the pollutants caused by vehicular traffic is traffic noise, which is likely to cause various negative health effects such as increased stress levels and blood pressure. In prior studies, individuals’ exposure to community noise has usually been assessed only with respect to home location, as required by national and international policies. However, these static exposure assessments most likely ignore a substantial share of individuals’ total daily noise exposure that occurs while they are on the move. Hence, new methods are needed for both assessing and reducing journey-time exposure to traffic noise as well as to other pollutants. In this study, I developed a multifunctional routing application for 1) finding shortest paths, 2) assessing dynamic exposure to noise on the paths and 3) finding alternative, quieter paths for walking. The application uses street network data from OpenStreetMap and modeled traffic noise data of typical daytime traffic noise levels. The underlying least cost path (LCP) analysis employs a custom-designed environmental impedance function for noise and a set of (various) noise sensitivity coefficients. I defined a set of indices for quantifying and comparing dynamic (i.e. journey-time) exposure to high noise levels. I applied the developed routing application in a case study of pedestrians’ dynamic exposure to noise on commuting related walks in Helsinki. The walks were projected by carrying out an extensive public transport itinerary planning on census based commuting flow data. In addition, I assessed achievable reductions in exposure to traffic noise by taking quieter paths with statistical means by a subset of 18446 commuting related walks (OD pairs). The results show significant spatial variation in average dynamic noise exposure between neighborhoods but also significant achievable reductions in noise exposure by quieter paths; depending on the situation, quieter paths provide 12–57 % mean reduction in exposure to noise levels higher than 65 dB and 1.6–9.6 dB mean reduction in mean dB (compared to the shortest paths). At least three factors seem to affect the achievable reduction in noise exposure on alternative paths: 1) exposure to noise on the shortest path, 2) length of the shortest path and 3) length of the quiet path compared to the shortest path. I have published the quiet path routing application as a web-based quiet path routing API (application programming interface) and developed an accompanying quiet path route planner as a mobile-friendly web map application. The online quiet path route planner demonstrates the applicability of the quiet path routing method in real-life situations and can thus help pedestrians to choose quieter paths. Since the quiet path routing API is open, anyone can query short and quiet paths equipped with attributes on journey-time exposure to noise. All methods and source codes developed in the study are openly available via GitHub. Individuals’ and urban planners’ awareness of dynamic exposure to noise and other pollutants should be further increased with advanced exposure assessments and routing applications. Web-based exposure-aware route planner applications have the potential to help individuals to choose alternative, healthier paths. When developing exposure-based routing analysis further, attempts should be made to enable simultaneously considering multiple environmental exposures in order to find overall healthier paths.
  • ijäs, timo (2021)
    The topic of this thesis is spatial analytics in competitive gaming and e-sports. The way in which players analyze spatial aspects of gameplay has not been well documented. I study how game, genre and skill level affect the use of spatial analysis in competitive gaming. My aim is also to identify the benefits and challenges of spatial analytics, as well as the need for new spatial analytical tools. Four games of different popular competitive gaming genres were chosen for the study. An online survey was conducted which resulted in a cross-sectional dataset of 2453 responses. It was analyzed using ordinal logistic regression and histogram-based gradient boosting in a cross-validating manner. Open-field answers were summarized using state-of-the-art deep learning methods and analyzed with inductive content analysis. Additionally, experts of each game were interviewed. The results show that the use and understanding of spatial analysis is largely not game- or genre dependent. Players grow spatial skills along with their skill level and start using more complex spatial analytical methods more frequently as their skill level rises. It is exceedingly rare that expert players do not analyze spatial aspects of their gameplay. There is a need for different kinds of spatial analytics tools in all competitive games, and the benefits of advanced tools to a player and the community can be large. However, the tools need to be highly contextualized, fine-tuned for each game specifically, and tailored to the players’ needs. Creating new tools for spatial analytics is something useful for competitive gaming as a whole. The inclusion of more detailed spatial analytical tools can lead to a new era of competitive gaming. E-sports is a rapidly growing phenomenon, and the analytics that support its growth should follow.
  • Korpelainen, Taiga (2023)
    Savanna rangelands are vital for millions of people for their livelihoods, food, and income in addition to protecting biodiversity and ecosystem services. Savannas are grazed by both livestock and wildlife, and due to population growth, livestock production and overgrazing is increasing. In addition, more people are migrating to savannas and converting them to cropland. Cropland expansion and grazing have increased also in the lowlands of Taita Hills, Kenya. The declining grass cover leads to, e.g., lower resources for livestock and wildlife, putting the future of this unique biome and its populations at risk. The study area consists mainly of the LUMO Community Wildlife Sanctuary (LUMO) and Taita Hills Wildlife Sanctuary (THWS) of which the first is grazed by both livestock and wildlife while the latter primarily by wildlife. The region experiences bimodal precipitation with long rains in March–May and short rains in October–December. However, the region experienced low rainfall throughout most months of 2022 resulting in exceptionally low grass cover. While this was an unusual year, there is no guarantee that this would not become the new normal with climate change incxreasingly affecting the region. Satellite remote sensing is a cost-effective way of monitoring changes in savanna rangelands due to its high spatio-temporal nature. This study included the use of remotely sensed spectral information together with field photographs of 36 plots in two different months, January and May 2022, to create a model that predicts monthly grass cover in the study area in 2022. A color index, Excess Greenness, was used to model the green fractional vegetation cover of field photographs by utilizing a manual grass cover classification. These modelled photographs were then used to upscale the model to very-high-resolution satellite imagery of January and May. The resulting grass cover map of May was used to train the model against various spectral predictors to create a model for predicting grass cover using medium-resolution satellite imagery. The model was validated by the very-high-resolution grass cover map from January, and the performance was promising (R2 = 0.89). The model produced grass cover maps for each month of 2022. The maps present different spatial patterns between LUMO and THWS and temporal patterns between months. As expected, THWS has higher grass cover than LUMO as it is only grazed by wildlife. Throughout the year there is a clear declining trend in grass cover. The original hypothesis was that there is clear monthly variability in grass cover. However, the field campaign in May resulted in unprecedently similar results as in January, i.e., low grass cover, which is also evident in the resulting monthly maps. The results of the model are promising as they provide evidence of the capabilities of the used method. The results also demonstrate the usability of an ordinary digital camera as the basis for vegetation cover models. In addition, this method can be applied to other temporal scales, for example for a yearly time series analysis. The grass cover maps can be further analyzed together with information of grazing pressure and used to inform decision-makers and other entities of land use management.
  • Sädekoski, Niklas (2020)
    Soil is the largest actively cycling terrestrial carbon pool, which has been severely distrubed in the last 100-200 years by human actions. To improve the situation, extensive monitoring of soil carbon and new methods for monitoring are required. This study demonstrates the capability of a portable hyperspectral device operating in the visible-near infrared (VIS-NIR) spectrum for soil organic carbon (SOC) prediction. Two multivariate methods, partial least squares regression (PLSR) and for this purpose previously untested lasso regression were used for prediction. 191 soil samples were collected from Taita Hills, Kenya. The samples represent a tropical altitudinal gradient with five land uses: agroforestry, field, forest, shrubland and sisal plantation. The samples were imaged with hyperspectral camera, Specim IQ in laboratory and in field conditions, and the carbon content of the samples was determined with a dry-oxidization analyzer. Three datasets were derived from the images, one containing the mean spectra of the complete imaged samples, one with segmented sub-image spectra and one with segmented sub-image spectra where outlier spectra were removed. Both multivariate methods were tested with all three datasets with good prediction accuracies (PLSR: R2min = 0.85, RMSEmin = 0.78, lasso: R2min=0.85, RMSEmin=0.80), demonstrating the feasibility of both the device and lasso regression as SOC prediction tools. Using the segmented sub-image datasets improved the results with PLSR but had no significant effect on lasso regression prediction results. While good results were gained with laboratory imagery, the field imaging conditions were difficult, and the data performed poorly. Future research should focus on finding solutions to reliably estimate SOC content in situ or with portable laboratory setups to make SOC measurements more widely accessible and agile for e.g. precision agriculture purposes.
  • Koivisto, Sonja (2021)
    Being physically active is one of the key aspects of health. Thus, equal opportunities for exercising in different places is one important factor of environmental justice and segregation prevention. Currently, there are no openly available scientific studies about actual physical activities in different parts of the Helsinki Metropolitan Area other than sports barometers. In the lack of comprehensive official data sources, user-generated data, like social media, may be used as a proxy for measuring the levels and geographical distribution of sports activities. In this thesis, I aim to assess 1) how Twitter tweets could be used as an indicator of sports activities, 2) how the sports tweets are distributed spatially and 3) which socio-economic factors can predict the number of sports tweets. For recognizing the tweets related to sports, out of 38.5 million tweets, I used Named Entity Matching with a list of sports-related keywords in Finnish, English and Estonian. Due to the spatial nature of my study, I needed tweets that contain a geotag, meaning that the tweet is attached to coordinates that indicate a location. However, only about 1% of tweets contain a geotag, and since 2019 Twitter doesn’t support precise geotagging anymore with some exceptions. Therefore, I implemented geoparsing methods to search for location names in the text and transform them to coordinates if the mentioned place was within the study area. After that, I aggregated the posts to postal code areas and used statistical and spatial methods to measure spatial autocorrelation and correlation with different socio-economic variables to examine the spatial patterns and socio-economic factors that affect the tweeting about sports. My results show that the sports tweets are concentrated mainly in the center of Helsinki, where the population is also concentrated. The distribution of the sports tweets exhibits local clusters like Tapiola, Leppävaara, Tikkurila and Pasila besides the largest cluster in the center of Helsinki. Sports-wise mapping of the tweets reveals that for example racket sport and skiing tweets are heavily concentrated around the corresponding facilities. Statistical analyses indicate that the number of tweets per inhabitant does not correlate with the education level or the amount of average income in the postal code area. The factors that predict the number of tweets per inhabitant are number of sports facilities per inhabitant, employment, and percentage of children (0-14 years old) in the postal code area. Keys to a successful study when analyzing Twitter data are geoparsing, having enough data, and a good language model to process it. Despite the promising results of this study, Twitter as indicator of physical activity should be studied more to better understand the kind of bias it inherently has before basing real-life decisions on Twitter research.
  • Aagesen, Håvard Wallin (2021)
    The Nordic region is a connected region with a long history of cooperation, shared cultures, and social and economic interactions. Cross-border cooperation and cross-border mobility has been a central aspect in the region for over half a century. Despite of shared borders and all countries being part of the Schengen Area, providing free movement, little research has been made on the extent of daily cross-border movements and little data exists on the topic. In light of the COVID-19 pandemic, human mobility and cross-border mobility has risen to the top of the political agenda, with new challenges changing cross-border mobility around the world. As an already very connected region, the Nordic region saw a sudden decrease in mobility and areas across borders were suddenly isolated from each other. The spread of the COVID-19 virus and the most important measures to counter the pandemic have been spatial in their nature. Restrictions on mobility and lockdown of regions and countries have been some of the measures set in place at varying degrees in different locations. Understanding the effects of mobility on the spread of COVID-19 and understanding how successful different measures have been is important in handling the ongoing and future pandemics. There is a lack of, particularly quantitative, research that investigates the functional aspects of cross-border mobility in the Nordic region. In addition, a lack of up-to-date, reliable data on human flows between the Nordic countries is missing. Research on the spread and effects of the COVID-19 pandemic in relation to human mobility, is rapidly increasing and being pioneered in conjunction with the developments of the pandemic. Through a lens of human mobility and activity spaces, how the cross-border regions in the Nordics reveal themselves by aggregating movements of individuals are investigated. The aim is to examine how geotagged Twitter data can be used to study cross-border mobility, as well as which functional cross-border areas can be estimated from movements of Twitter users and how these movements have been affected by the COVID-19 pandemic. Twitter data is collected and processed and reveal human mobility flows from before and after COVID-19 travel restrictions were set in place, making the data fit for a correlation analysis with available official commuter statistics. Using a kernel density estimation, estimations of the functional cross-border regions at different spatial levels are conducted, uncovering the spatial extent of functional regions and how human mobility connects regions across national borders. On this basis, movements of Twitter users in two time periods, March 2019 – February 2020 and March 2020 – February 2021, are compated with available statistics from the Nordic region. The results show that Twitter data correlates strongly with official commuter statistics for the region and are a good fit for studying cross-border mobility. Additionally, policy made cross-border regions does not completely overlap with the functional cross-border regions. Although there are many similarities between the policy made and functional cross-border regions, in a functional aspect the regions are smaller than the policy made regions and heavily condensed around large cities. The estimation of functional cross-border regions also show the effect of COVID-19 and measures taken to limit cross-border mobility. The amount of cross-border mobility is severely reduced and the composition of functional regions changes differently for different regions. In general, the spatial extent of cross-border regions reduce and gravitates towards the largest cities on either side of the border. The methods and results developed in this thesis provides an understanding of the dynamics of mobility flows in the Nordic region, and are first steps in increasing the use of novel data sources in cross-border mobility research in the Nordics. Further research into methods for expanding the data basis in the region is needed and further research should be conducted in deepening the understanding of demographic and temporal aspects of functional cross-border regions. Regional planning, tourism, and statistics are all fields that rely on recent, up-to-date data, and the methods for utilizing novel data sources shown in this thesis can mitigate some of the flaws that current data sources have. In combating the spread of the COVID-19 virus, it is of profound importance to understand mobility flows across borders, something that this thesis provides methods and insights to do.
  • Hästbacka, Matti (2023)
    The direct economic impacts of the global tourism industry account for 4 % of global GDP and 8 % of global greenhouse gas emissions. The industry is in transformation caused by climate change, political instability and rapid technological development. In addition, the relationship between biodiversity conservation and tourism as well as the growing popularity are considered megatrends impacting the sector. Traditional mass tourism destinations, such as the Canary Islands, may start seeing new kinds of visitors, if traveling to exotic destinations becomes difficult as a result of these transformations. Understanding transformations affecting tourism requires information about tourists’ mobilities, interests and preferences. However, traditional data collection methods may not necessarily be suited for studying quickly changing tourism. The need for Information about visitations to natural and protected areas is especially high, as traditional tourism indicators, such as flights and accommodation statistics do not tell where the tourists spend time. Social media data may enable production of new kind of knowledge and studying nature-based tourism in a new way. In this thesis, I intent to assess the role of nature in tourism in the Canary Islands, Spain using data from the photo-sharing platform Flickr. First, I compare the spatiotemporal patterns of Flickr data against official data about tourism flows to confirm the feasibility of Flickr as a data source in the Canary Islands context. I then try to understand the importance of nature visitations and differences in nature visitation patterns between visitors from different countries. Finally, I turn to analyse contents of the images to see what kinds of nature-related topics are important for each group, making use of a deep learning and cluster detection algorithms. I verify the results of my empirical analysis with data collected through interviewing experts familiar with Canary Islands tourism. The results of my research show that Flickr reflects Canary Islands tourism patterns moderately well, and that it can be used to produce information about differences in nature visitation patterns. Protected areas are shown to be important and central for Canary Islands tourism, but differences in interest toward these areas between groups are notable. Results of the content analyses show that while differences between groups exist, both nature-related content and photos of humans are important in content posted from PAs. Verification data collected through expert interviews shows that the observed differences between groups correspond to the experts’ perceptions about differences between different groups. The findings of my thesis demonstrate the importance of nature and protected areas in Canary Islands tourism and confirm earlier knowledge about the use of Flickr in studying nature visitations. The results may inform future research in the Canary Islands. More broadly, they provide information about the feasibility and limitations of the use of social media data for nature-based tourism research.
  • Inkeröinen, Oula (2023)
    Horizontal visibility determines how far a person can see without any obstacles blocking their line of sight in a horizontal direction. Visibility is in great importance in numerous fields of study and lines of work, such as urban planning, wildlife surveillance and conservation and in military. Manmade obstacles, such as buildings, bridges or other built structures, and environmental elements, like topographical variations or vegetation, can cause noticeable obstruction of visibility in varying magnitude. Airborne laser scanning (ALS) based on light detection and ranging (LiDAR) technology has been used successfully to model built structures and environmental elements, such as forest inventory attributes. This study aims to find out how well horizontal visibility in Finnish boreal forests can be modelled and predicted using a predictive model based on 5 points/m2 ALS point cloud dataset provided by National Land Survey of Finland. The research was done in two separate locations, Karilasanden and Sipoonkorpi National Park, located in Uusimaa region in southern Finland. The ALS point clouds were scanned in summertime 2019 and 2020 for the two research areas. In addition, a total of 60 field measurements of horizontal visibility were collected in summertime 2023. Modelling of horizontal visibility was done with an area-based-approach on the ALS point cloud. The most suitable forest structure variables were selected using a linear regression model based on the measured data at the research areas. The variables were used to predict horizontal visibility with a wall-to-wall method for the whole study sites. Finally, map visualizations were created by generalizing vector polygons of horizontal visibility predictions, and by combining viewshed and horizontal visibility prediction results. The results indicate that number of forest structure metrics based on ALS data can be used for horizontal visibility modelling, including canopy density, median and lower quantile percentages of height distributions and relative density of points from 0.5 to 2.2-meter height. In addition, the 5 points/m2 ALS dataset from National Land Survey of Finland is sufficient dataset for estimation of horizontal visibility in Finnish boreal forests when the accuracy of the output is around tens of meters. Understandable and fit for use map visualization can be produced from the prediction results in various formats. Modelling of Finnish forest structure and using structure metrics as variables for modelling horizontal visibility can be useful for example in city planning for planning the structure of urban greenery or in planning phases of military operations. The available dataset is sufficient for rougher scale modelling, but to achieve finer scale modelling results, increasing the density of the point cloud could be tried or the ALS dataset could be accompanied with other datasets. Also, the availability of the data is somewhat limited since the data is not open. The combining of ALS datasets with TLS datasets for higher point cloud density, usage of TLS dataset for verification purposes of this research's methods or for gathering additional field measurements rose as possible future research topics, as well as the possibility of modifying the point cloud dataset in order to examine the most important forest structure variables more accurately.
  • Ojasalo, Amanda (2024)
    Climate change and urbanization are among the largest environmental challenges facing the world today. The role of vegetation in urban environments is substantial from the perspectives of climate, ecology, and human-wellbeing. Plant phenology plays a key role in the functionality and feedbacks related to these ecosystem services and the characteristics of urban phenology can considerably differ from rural areas due to Urban Heat Island (UHI), vegetation composition, hydrological changes, light pollution, and air pollutants. Several previous studies using coarse resolution remote sensing data have reported longer Growing Season Length (GSL) in urban areas compared to their rural counterpart and the UHI effect is generally considered as the main driver for these differences. However, urban phenology studies have not been implemented on a regional European scale and high-resolution remote sensing data is needed to understand the characteristics of heterogenous and sparse urban vegetation. Therefore, the objectives of this study were (1) to analyse GSL along the urban-rural gradients in 38 European capital cities using Copernicus HR-VPP phenology dataset on a 10-meter spatial resolution, (2) to analyse GSL along the urban-rural gradient between the 38 European capital cities, and (3) to find out how Land Surface Temperature (LST), land cover and dominant leaf type influence on the GSL variation. The GSL pattern along the urban-rural gradients were classified into six categories based on linear and quadratic fits. The results showed that the GSL along the gradient in European capital cities is highly variate. It shortens along the gradient in 8 cities and the urban GSL is longer in 26 cities when compared to the overall surrounding zone, contradictory to the general outcome of previous studies. The influence of LST, land cover and dominant leaf type was examined with a Geographically Weighted Regression (GWR) model which considers the spatial nonstationary of variables. The final GWR model variables included LST, the proportion of urban land cover above 30 % of sealed surface and the proportion of broadleaved trees which all had spatially varying and nonlinear influences on GSL. These results and the variate gradient patterns suggest that despite the significance of LST, GSL variation along the urban-rural gradient is more driven by changes in land cover and vegetation characteristics. Spatial modelling techniques are needed to understand these locally varying relationships. There are several potential methodological and site-related explanations for the divergent findings of this and previous studies. The key methodological difference is the better spatial resolution which improves the accuracy of GSL detection, agricultural land cover masking and urban area definition. Site-related explanations include the different background climate and vegetation types, urban vegetation composition and species, and urbanization intensity. In addition, several heatwaves took place during the study period potentially contributing to the early senescence of urban vegetation. This study highlights the need for a high-resolution remote sensing data when analysing urban vegetation phenology and provides new information about the complex dynamics of urban phenology in general level and in European capital cities. These results can be beneficial for developing sustainable cities where urban vegetation plays a key role in adapting and mitigating climatic, ecological, and societal challenges.