Browsing by Subject "Geoinformatiikka"

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  • Roiha, Johanna (Helsingin yliopisto, 2018)
    Tutkielmassa käsitellään arkeologisten kenttätöiden dokumentointia sekä pohditaan sen vuorovaikutusta kaivausmenetelmiin ja raportointiin. Keskeinen osa tutkielmaa on menetelmäkokeilu. Tutkielman tavoitteena on selvittää, voiko tasokarttoja tuottaa suoraan ortokuvista eri kuvaluokittelumenetelmillä ja mikä menetelmistä voisi soveltua tähän käyttötarkoitukseen parhaiten. Tutkielmassa pohditaan lisäksi kehittyvien dokumentointimenetelmien vaikutuksia tulevaisuuden työtapoihin ja muihin alan käytäntöihin. Menetelmäkokeilun aineistona on käytetty kuvia, jotka ovat peräisin neljältä eri arkeologiselta tutkimuskohteelta ja joista on tuotettu fotogrammetrialla 3D-malli. Kyseisistä malleista on puolestaan tuotettu ortokuvat, joihin luokittelutyökalujen toimivuutta on testattu ja lopputuloksia vertaillaan visuaalisesti. Menetelmäkokeiluun on valittu kolme eri kuvaluokittelumetodia: ohjattu luokittelu, ohjaamaton luokittelu ja oliopohjainen kuva-analyysi. Jokaiseen neljään ortokuvaan on testattu näitä kolmea eri luokittelumenetelmää. Menetelmät perustuvat algoritmeihin, jotka hyödyntävät kuvapikseleiden sävyarvoja ja niiden perusteella kuvista voidaan tuottaa rasterikarttoja. Tutkielman analyysien perusteella luokittelutyökaluja voidaan hyödyntää tasokarttojen tekemiseen suoraan ortokuvista. Lisää tutkimusta aiheesta kuitenkin tarvitaan ja lisätutkimuksella tulisi etenkin selvittää vielä tarkemmin, mikä luokittelumenetelmistä voisi parhaiten soveltua arkeologisten kenttätöiden dokumentointiin. Dokumentointitapojen kehitys tulevaisuudessa vaikuttaa arkeologisiin kenttätyömenetelmiin ja raportointiin. Kuvaluokittelutyökalujen etuna on niiden toistettavuus ja nopeus verrattuna perinteisiin dokumentointimenetelmiin, kuten karttojen piirtämiseen käsin millimetripaperille kenttätyötilanteessa. Kehittämällä kenttätöiden dokumentointimenetelmiä voidaan parantaa arkeologisten tiedon laatua ja hyödynnettävyyttä tulevaisuudessa
  • Vuorinne, Ilja (Helsingin yliopisto, 2020)
    Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation.
  • Aalto, Iris (Helsingin yliopisto, 2020)
    Global warming is expected to have detrimental consequences on fragile ecosystems in the tropics and to threaten both the global biodiversity as well as food security of millions of people. Forests have the potential to buffer the temperature changes, and the microclimatic conditions below tree canopies usually differ substantially from the ambient macroclimate. Trees cool down their surroundings through several biophysical mechanisms, and the cooling benefits occur also with trees outside forest. Remote sensing technologies offer new possibilities to study how tree cover affects temperatures both in local and regional scales. The aim of this study was to examine canopy cover’s effect on microclimate and land surface temperature (LST) in Taita Hills, Kenya. Temperatures recorded by 19 microclimate sensors under different canopy covers in the study area and LST estimated by Landsat 8 thermal infrared sensor (TIRS) were studied. The main interest was in daytime mean and maximum temperatures measured with the microclimate sensors in June-July 2019. The Landsat 8 imagery was obtained in July 4, 2019 and LST was retrieved using the single-channel method. The temperature records were combined with high-resolution airborne laser scanning (ALS) data of the area from years 2014 and 2015 to address how topographical factors and canopy cover affect temperatures in the area. Four multiple regression models were developed to study the joint impacts of topography and canopy cover on LST. The results showed a negative linear relationship between daytime mean and maximum temperatures and canopy cover percentage (R2 = 0.6–0.74). Any increase in canopy cover contributed to reducing temperatures at all microclimate measuring heights, the magnitude being the highest at soil surface level. The difference in mean temperatures between 0% and 100% canopy cover sites was 4.6–5.9 ˚C and in maximum temperatures 8.9–12.1 ˚C. LST was also affected negatively by canopy cover with a slope of 5.0 ˚C. It was found that canopy cover’s impact on LST depends on altitude and that a considerable dividing line existed at 1000 m a.s.l. as canopy cover’s effect in the highlands decreased to half compared to the lowlands. Based on the results it was concluded that trees have substantial effect on both microclimate and LST, but the effect is highly dependent on altitude. This indicates trees’ increasing significance in hot environments and highlights the importance of maintaining tree cover particularly in the lowland areas. Trees outside forests can increase climate change resilience in the area and the remaining forest fragments should be conserved to control the regional temperatures.
  • Todorovic, Sara; Rekola, Hanna; Muukkonen, Petteri; Bernelius, Venla (Helsingin kaupunki, 2020)
    Helsingin kaupungin pelastuslaitoksen julkaisuja
  • Ehnström, Emil Mattias (Helsingin yliopisto, 2021)
    The number of people belonging to a language minority in Finland is increasing and people are becoming more and more spatially mobile. This has also led to an increase in transnationals and higher rates of cross-border mobility. With new methods involving social media big data, we can map spatial mobility patterns in new ways and deepen the understanding of how people relate to space. Differences in spatial mobility can for example give us an indication of the rate of integration into society. Some claim that a more spatially mobile life is a sign of success, but can we see differences in spatial mobility between people in Finland? The three language minorities considered in this thesis are Swedish, Russian, and Estonian. The history and culture of these groups are different as well as their status in Finnish society. Swedish speakers, with a national language status, have a different role in society, but do this well integrated minority differ from the other ones spatially? By using Twitter data and looking at the spatial mobility within Finland, we see where differences occur between language groups. To understand how strong ties the language groups have with neighbouring countries, we look at cross-border mobility to Estonia, Russia, and Sweden. The results show that there are differences in the spatial mobility of language minorities in Finland. Estonian speakers most frequently visit Estonia, while at the same time they are less mobile within Finland. The variation was large for Russian speakers, with some visiting Russia often and others almost never. Swedish speakers seem to have relatively weak ties to Sweden, compared to the other language groups and have very similar spatial mobility to the majority Finnish speaking population.
  • Tenkanen, Henrikki Toivo Olavi (Department of geosciences and geography, 2017)
    Department of Geosciences and Geography A
  • Lämsä, Suvi (Helsingin yliopisto, 2021)
    Urban environments are constantly changing and expanding. They grow, evolve, and adapt to society and residents’ needs. Environmental changes have an impact also on urban green such as trees. This is because the increase of building stock and expanding cityscape will target these green spaces. However, the significance of those green spaces is understood as they have a positive impact on the residents’ well-being and health. For example, urban trees are known to improve the air quality and to provide mentally relaxing environments for residents. As this importance is emphasized, changes in the areas must be monitored, which increases the importance of the change detection studies. Change detection is a comparison of two or more datasets from the same area but at different times. Principally, changes have been detected with various remote sensing methods, such as aerial- and satellite images, but as airborne laser scanning technology and multi-temporal laser scanning datasets have become more common, the use of laser scanning data has also increased. The advantage of the laser scanning method is especially in its ability to produce three-dimensional information of the area. Therefore, also vertical properties can be studied. The method’s advantage is its ability to detect changes in urban tree cover as well as in tree height. The aim of this study was to investigate how tree cover and especially canopy height have changed in the Kuninkaantammi area in Helsinki during 2008‒2015, 2015‒2017, 2017‒2020, and 2008‒2020 from multi-temporal laser scanning data. One of the starting points of this study was to find out how airborne laser scanning datasets with different sensors and survey parameters are suitable for change detection. Also, what kind of problems the differences between datasets will raise and how to reduce those problems. The study used laser scanning data from the National Land Survey of Finland and from the city of Helsinki for four different years. The canopy height models were produced of each dataset and changes were calculated as the difference of each canopy height model. The results show that multi-temporal laser scanning data require a lot of manual processing to create datasets comparable. The greatest problems were differences in point density and in classification of the data. The sparse data from the National Land Survey of Finland affected how changes were managed to be studied. Therefore, changes were detected only in general level. In addition, each dataset was classified differently which affected the usability of the classes in the datasets. The problems encountered were reduced by manual work like digitizing or by masking non-vegetation objects. The results showed that the change in the Kuninkaantammi area has been relatively large at the time of the study. Between 2008 and 2015, 12.1% of the tree cover was lost, 9.9% between 2015 and 2017, and 13.2% between 2017 and 2020. In addition, an increase in canopy height was detected. Between 2008 and 2015, 44.2% of the area had greater than 2 m increase in canopy height. Similarly, increase occurred in 11.1% and 3.5% of the area in 2015‒2017 and in 2017‒2020, respectively. Although the changes were observed at a general level, it can be concluded that the used datasets can provide valuable information about the changes in urban green that have taken place in the area.
  • Garedew, Weyessa; Tesfaw Hailu, Binyam; Lemessa, Fikre; Pellikka, Petri; Pinard, Francois (Springer International Publishing AG, 2017)
    Climate Change Management
  • Toikka, Akseli (Helsingin yliopisto, 2019)
    Urban vegetation has traditionally been mapped through traditional ways of remote sensing like laser scanning and aerial photography. However, it has been stated that the bird view examination of vegetation cannot fully represent the amount of green vegetation that the citizens observe on street level. Recent studies have raised human perspective methods like street view images and measuring of green view next to more traditional ways of mapping vegetation. Green view index states the percentage of green vegetation in street view on certain location. The purpose for this study was to create a green view dataset of Helsinki city through street view imagery and to reveal the differences between human perspective and aerial perspective in vegetation mapping. Street view imagery of Helsinki was downloaded from Google street view application interface. The spatial extent of the data was limited by the availability of street view images of summer months. Several green view maps of Helsinki were created based on the green view values calculated on the street view images. In order to understand the differences between human perspective and the aerial view, the green view values were compared with the regional land cover dataset of Helsinki trough linear regression. Areas with big differences between the datasets were examined visually through the street view imagery. Helsinki green view was also compared internationally with other cities with same kind of data available. It appealed that the green view of Helsinki was divided unequally across the city area. The lowest green view values were found in downtown, industrial areas and the business centers of the suburbs. Highest values were located at the housing suburbs. When compared with the land cover, it was found that the green view has a weak correlation with low vegetation and relatively high correlation with taller vegetation such as trees. Differences between the datasets were mainly concentrated on areas where the vegetation was not visible from the street by several reasons. Main sources of errors were the oldest street view images and the flaws in image classification caused by other green objects and shadows. Even though Helsinki has many parks and other green spaces, the greenery visible to the streets isn’t always that high. The green view dataset created in this study helps to understand the spatial distribution of street greenery and brings human perspective next to more traditional ways of mapping city vegetation. When combined with previous city greenery datasets, the green view dataset can help to build up more holistic understanding of the city greenery in Helsinki
  • Rantanen, Olli (Helsingin yliopisto, 2020)
    Uuden tieliikennelain mukanaan kunnille tuomat velvoitteet, kuten liikenteenohjaukseen käytetyn välineistön (esim. liikennemerkkien) ylläpitovastuu, siirtyy kunnille 1.6.2020. Kenttäinventoimalla suoritettava liikennemerkkien kunnon ja sijainnin selvittäminen on usein työlästä ja tuottaa kustannuksia. Tässä tutkimuksessa selvitetään, miten näitä voidaan automatisoidusti inventoida panoraamakuvilta. Samalla verrataan panoraamakuvien ja niistä luotujen osakokonaisuuksien (pilkottujen kuvien) soveltuvuutta kyseiseen tarkoitukseen. Tunnistuksen tuloksena syntyviä havaintoja verrataan Väyläviraston ylläpitämään avoimeen liikennemerkkiaineistoon sekä tunnistettujen kohteiden sijainti lasketaan kuvilta. Työssä tutustutaan myös eri kohteentunnistusalgoritmien toimintaan sekä selvitetään, miten liikennemerkkien automaattisessa tunnistuksessa on onnistuttu muissa tutkimuksissa. Aineistona toimii Inkoosta otettujen panoraamakuvien lisäksi Mapillaryn toimittamat kuva-aineistot, joita käytetään YOLOv3-kohteentunnistusalgoritmin koulutukseen ja testaukseen. Työssä esitellään myös YOLOv3-koulutuksen toteuttaminen ja käydään läpi tarvittavat ohjelmistot sen implementoinnissa toiseen työhön. Koulutus vaatii riittävän GPU:n lisäksi erilaisia ohjelmia sekä runsaasti kuva-aineistoa, jotta ylisovittamisen riskiltä vältytään. Tulosten perusteella pilkotut kuvat tuottavat paremman tuloksen verrattuna panoramakuviin. Pilkotuilta kuvilta jokainen ajoreitin varrella ollut kärkikolmio tunnistettiin, kun taas panoraamakuvilta tämä ei onnistunut. Lisäksi algoritmin kyky sijoittaa kärkikolmion sijainti kuvalle oli varsin hyvä saavuttaen keskimäärin IoU-arvon 0,86, kun se panoraamoilla oli 0,52. Samoin tulosten luotettavuutta kuvaavat Precision- ja Recall-arvot olivat huomattavasti korkeammat kuin panoraamakuvilla. Työssä havaittiin lisäksi, että Väyläviraston avoimesta aineistosta puuttuu useita kärkikolmioita. Kuvilta onnistuttiin myös laskemaan muutaman metrin tarkkuudella kärkikolmioiden sijainti maastossa. Tutkimuksen perusteella kohteentunnistusalgoritmit tuottavat merkittävää hyötyä kohteiden automaattisessa tunnistuksessa. Algoritmien hyödyntämistä tulevaisuudessa mahdollistaa lisääntyvä kuva-aineistojen määrä sekä laskentatehon kasvu. Hyödyntämällä kohteentunnistusalgoritmeja kuntien on mahdollista helpottaa uuden tieliikennelain velvoitteiden noudattamista. Tämän myötä algoritmien suosio voi kasvaa tulevaisuudessa. Kohteentunnistusalgoritmien implementointiin tarvitaan kuitenkin ohjeistusta ja käyttötapauksia, joita tämä tutkimus tuloksillaan edistää.
  • Todorovic, Sara (Helsingin yliopisto, 2020)
    Fires in residential buildings can lead to significant personal injury and property damage, especially in cities. Fire incidence has been found to have a strong connection with the characteristics of neighbourhoods and their inhabitants, such as with socioeconomic status and the features of households and buildings. However, the influencing factors are complex and often interconnected, which has made it difficult to make accurate predictions. Risk modelling and spatial data analysis provide effective and practical means of studying the phenomenon, especially from the point of view of accident prevention and preparedness. To date, knowledge of the spatial risk factors affecting residential fire incidence is yet limited in Helsinki. Thus, this study has sought to bring new empirical evidence on the matter. This study analysed residential fires in Helsinki from 2014 to 2018 at a 250 x 250 m grid level. The spatial dependence of fires was investigated by observing statistically significant clusters of fires. In this study, a risk model was created that sought to identify the underlying structural, socioeconomic, and household characteristics of neighbourhoods that affect the likelihood of residential fire incidence. The methods used were linear regression and the Geographically Weighted Regression (GWR), which takes spatial heterogeneity into account. The results showed that residential fires are spatially clustered in Helsinki. A significant large concentration of fires was found in the inner-city area and smaller concentrations in eastern Helsinki. The results indicate that the structural features of the neighbourhoods, socioeconomic status, and household circumstances have an impact on the likelihood of residential fire incidence by both increasing and decreasing the risk of fire. At the neighbourhood level, statistically significant explanatory variables that increased fire risk were population density, low education, unemployment, occupancy rate of dwellings, and home ownership. A negative relationship with fire risk was found with residential building density, age of the buildings, high education, as well as home ownership. Overall, in the study area, these eight variables explained about half of the variance of residential fire incidence. In a comparison between the models, the explanatory power of the GWR was better than linear regression, and it was also able to identify significant local variations in the effects of explanatory variables on fire risk. A comprehensive understanding of the factors influencing residential fire risk at local levels is important for rescue services, especially in terms of planning response readiness and efficient allocation of resources. In the future, more precise models should be developed in order to achieve a more comprehensive understanding of fire risk and the factors affecting it. Particular attention should be paid to the use of more precise and diverse data and methods in modelling, as well as to the temporal dimension and the consequences of fires.
  • Krötzl, Julius (Helsingin yliopisto, 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 (Helsingin yliopisto, 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.
  • Helle, Joose (Helsingin yliopisto, 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 (Helsingin yliopisto, 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.
  • Sädekoski, Niklas (Helsingin yliopisto, 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 (Helsingin yliopisto, 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 (Helsingin yliopisto, 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.
  • Willberg, Elias; Järv, Olle; Väisänen, Tuomas Lauri Aleksanteri; Toivonen, Tuuli (Helsingin yliopisto, Kaupunkitutkimusinstituutti Urbaria, 2021)
    Urbaria Summaries Series