Browsing by study line "Geoinformatik"

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.

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.

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.

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.

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.

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.

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.