Browsing by Subject "Geoinformatiikka"

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  • Lehtonen, Pyry (Helsingin yliopisto, 2021)
    Geographical accessibility to sports facilities plays an important role when choosing a sports facility. The aim of my thesis is to examine geographical accessibility for sports facilities in Helsinki and Jyväskylä. The data of my study consists of the facilities of three different types of sports in Helsinki, Jyväskylä. The chosen types of facilities are ball parks, disc golf courses and fitness centers. I also use demographic data that cover the age groups of 7-12, 20-24 and 60-64. Mapple Analytics Ltd has produced geographical accessibility data covering whole of Finland which I also use as my data. In my thesis I analyzed geographical accessibility of sports facilities and compare the results to demographic data. Both the geographical accessibility data and demographic data is in 250 x 250 m grid level. the methods I used were Local Moran’s I and Bivariate Local Moran’s I. I applied the methods so that I combined the travel-time data and demographic data. The travel-times are from Mapple Insights API. The travel modes I have used are cycling and driving because people travel to sports facilities mostly by driving or by active methods, especially cycling. The travel-times to ball parks and fitness centers are overall good in both study regions. The good geographical accessibility is caused by that the service pattern is so dense for ball parks and fitness centers. The service pattern covers almost all of the inhabited area in both study regions. However, for some postal areas seem to have not so good geographical accessibility to ball parks. In some areas in Helsinki the geographical accessibility to disc golf course can be considered to be somewhat bad. For the chosen age groups only 20-24-year-olds have unsatisfactory travel-times to disc golf course either by cycling or driving. Other age groups do not show a similar pattern because of the different service pattern of ball parks and fitness centers. Demographic variables do not explain the travel times in this context. It is important to see which postal areas have good or bad geographical accessibility to sports facilities. This helps the future planning of sports facilities. In the future it is also possible to apply non spatial methods to the data I have collected or a similar dataset. It would also be possible to which demographic variable best explains travel-times. Because of Mapple Insighs API data is in 250 x 250 m grid level many applications can be developed using the data.
  • 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.
  • Hirvonen, Hanna (Helsingin yliopisto, 2022)
    The African savanna elephant (Loxodonta africana) as a renowned “ecosystem engineer” modifies its habitat by sometimes destroying woody vegetation. Their destructive effect intensifies during the dry seasons, when they form larger herds and seek to consume woody plants, especially near permanent water sources. If this happens season after season in a restricted area, such as a wildlife reserve, the tree cover is reduced. Since elephants tend to make smaller trees to fall more easily than the larger ones, this “elephant problem” harms the regeneration ability of the ecosystem in a long run, even turning savannas into grasslands. With less and less trees available, elephants and other fauna in conservation areas could end up being at a fatal risk. Multi-scale vegetation structure can be studied with airborne (ALS) and terrestrial laser scanning (TLS). Although both types of LiDAR have been applied in studies on trees, most of the ALS studies concern biomass and none of the TLS research cover elephants. Tree structure on the individual tree level can be modelled using TreeQSM modelling that has not yet been applied in savanna vegetation. This study can be considered pioneering as it attempts to provide answers to these two study questions: (1) How does tree density derived from airborne laser scanning data correlate with elephant density, elephant path proximity, and river proximity? (2) How do tree architecture metrics derived from terrestrial laser scanning data correlate with elephant path proximity and river proximity? The study area is Taita Hills Wildlife Sanctuary, a small privately-owned wildlife conservancy in southeastern Kenya that falls within an area scanned with ALS in 2014. The vegetation of the reserve has been changing for many decades, and the latest changes in the vegetation cover are visible from satellite images. The “elephant problem” near the area was scientifically discussed already in 1960’s, so their damage may have been taking place for a long time. There are two datasets from the area for estimating elephant occurrence (elephant density based on elephant observation points and elephant track proximity based on elephant tracks) and one for the proximity to the river. Tree density was calculated based on detected treetops from the ALS point cloud and its correlations between the elephant predictors and the river proximity was analyzed. TLS measurements of 72 individual trees of Vachellia tortilis and Newtonia hildebrandtii were made in January and February 2020 in Taita Hills Wildlife Sanctuary. 53 were successfully modelled with TreeQSM. The correlations between the tree structure metrics and elephant density, elephant track proximity, and the river proximity were analyzed. The values for crown ratio, the metric that correlated significantly with the elephant track proximity were predicted to assess the meaning of the results in practice. The overall findings from both analyses (ALS and TLS) may suggest that trees in Taita Hills Wildlife Sanctuary may have suffered from elephant damage, since lower tree density correlates with both the elephant density estimates and the elephant track proximity. The trees scanned with TLS seem to be somewhat larger in closer proximities to the elephant tracks, while smaller trees are more able to survive in areas further away. Quantifying elephant damage in more detail, such as torn or hanging branches, was still not achieved by this study. Regardless, it can be concluded that there is enough foundation for further research on the important issue, the phenomenon that can turn dangerous to many species that were supposed to be protected.
  • 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 (University of Helsinki, Faculty of Science, Department of Geosciences and Geography, 2017)
    Department of Geosciences and Geography. A
    Understanding the spatial patterns of accessibility and mobility are a key (factor) to comprehend the functioning of our societies. Hence, their analysis has become increasingly important for both scientific research and spatial planning. Spatial accessibility and mobility are closely related concepts, as accessibility describes the potential to move by modeling, whereas spatial mobility describes the realized movements of individuals. While both spatial accessibility and mobility have been widely studied, the understanding of how time and temporal change affects accessibility and mobility has been rather limited this far. In the era of ‘big data’, the wealth of temporally sensitive spatial data has made it possible, better than ever, to capture and understand the temporal realities of spatial accessibility and mobility, and hence start to understand better the dynamics of our societies and complex living environment. In this thesis, I aim to develop novel approaches and methods to study the spatio-temporal realities of our living environments via concepts of accessibility and mobility: How people can access places, how they actually move, and how they use space. I inspect these dynamics on several temporal granularities, covering hourly, daily, monthly, and yearly observations and analyses. With novel big data sources, the methodological development and careful assessment of the information extracted from them is extremely important as they are increasingly used to guide decision-making. Hence, I investigate the opportunities and pitfalls of different data sources and methodological approaches in this work. Contextually, I aim to reveal the role of time and the mode of transportation in relation to spatial accessibility and mobility, in both urban and rural environments, and discuss their role in spatial planning. I base my findings on five scientific articles on studies carried out in: Peruvian Amazonia; national parks of South Africa and Finland; Tallinn, Estonia; and Helsinki metropolitan area, Finland. I use and combine data from various sources to extract knowledge from them, including GPS devices; transportation schedules; mobile phones; social media; statistics; land-use data; and surveys. My results demonstrate that spatial accessibility and mobility are highly dependent on time, having clear diurnal and seasonal changes. Hence, it is important to consider temporality when analyzing accessibility, as people, transport and activities all fluctuate as a function of time that affects e.g. the spatial equality of reaching services. In addition, different transport modes should be considered as there are clear differences between them. Furthermore, I show that, in addition to the observed spatial population dynamics, also nature’s own dynamism affects accessibility and mobility on a regional level due to the seasonal variation in river-levels. Also, the visitation patterns in national parks vary significantly over time, as can be observed from social media. Methodologically, this work demonstrates that with a sophisticated fusion of methods and data, it is possible to assess; enrich; harmonize; and increase the spatial and temporal accuracy of data that can be used to better inform spatial planning and decision-making. Finally, I wish to emphasize the importance of bringing scientific knowledge and tools into practice. Hence, all the tools, analytical workflows, and data are openly available for everyone whenever possible. This approach has helped to bring the knowledge and tools into practice with relevant stakeholders in relation to spatial planning.
  • 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
  • Leppämäki, Tatu (Helsingin yliopisto, 2022)
    Ever more data is available and shared through the internet. The big data masses often have a spatial dimension and can take many forms, one of which are digital texts, such as articles or social media posts. The geospatial links in these texts are made through place names, also called toponyms, but traditional GIS methods are unable to deal with the fuzzy linguistic information. This creates the need to transform the linguistic location information to an explicit coordinate form. Several geoparsers have been developed to recognize and locate toponyms in free-form texts: the task of these systems is to be a reliable source of location information. Geoparsers have been applied to topics ranging from disaster management to literary studies. Major language of study in geoparser research has been English and geoparsers tend to be language-specific, which threatens to leave the experiences provided by studying and expressed in smaller languages unexplored. This thesis seeks to answer three research questions related to geoparsing: What are the most advanced geoparsing methods? What linguistic and geographical features complicate this multi-faceted problem? And how to evaluate the reliability and usability of geoparsers? The major contributions of this work are an open-source geoparser for Finnish texts, Finger, and two test datasets, or corpora, for testing Finnish geoparsers. One of the datasets consists of tweets and the other of news articles. All of these resources, including the relevant code for acquiring the test data and evaluating the geoparser, are shared openly. Geoparsing can be divided into two sub-tasks: recognizing toponyms amid text flows and resolving them to the correct coordinate location. Both tasks have seen a recent turn to deep learning methods and models, where the input texts are encoded as, for example, word embeddings. Geoparsers are evaluated against gold standard datasets where toponyms and their coordinates are marked. Performance is measured on equivalence and distance-based metrics for toponym recognition and resolution respectively. Finger uses a toponym recognition classifier built on a Finnish BERT model and a simple gazetteer query to resolve the toponyms to coordinate points. The program outputs structured geodata, with input texts and the recognized toponyms and coordinate locations. While the datasets represent different text types in terms of formality and topics, there is little difference in performance when evaluating Finger against them. The overall performance is comparable to the performance of geoparsers of English texts. Error analysis reveals multiple error sources, caused either by the inherent ambiguousness of the studied language and the geographical world or are caused by the processing itself, for example by the lemmatizer. Finger can be improved in multiple ways, such as refining how it analyzes texts and creating more comprehensive evaluation datasets. Similarly, the geoparsing task should move towards more complex linguistic and geographical descriptions than just toponyms and coordinate points. Finger is not, in its current state, a ready source of geodata. However, the system has potential to be the first step for geoparsers for Finnish and it can be a steppingstone for future applied research.
  • Saarimaa, Saku (Helsingin yliopisto, 2022)
    Recent studies on day-care staff have reported on problems in hiring qualified staff, and in increased resignations in existing staff. These problems are connected to an increase in workload and stress, and reduced wellbeing at work. When workload and challenges in day-care work increase, there can even be a risk of diminishing the pedagogical quality of education. The problems seem to occur differently and in different magnitudes in different day-care units, which indicates learning conditions’ possible segregation. In the case of schools, the socioeconomic status of nearby population has been noticed to affect children’s predisposed abilities to learn, and their support requirements in learning. This effect can be assumed to affect early childhood education similarly, which would lead to day-cares in socioeconomically disadvantaged areas to require extra resources and staff to compensate for the children’s increased support requirements. If those extra resources are not available, the staff will experience increased workload and stress, which will cause problems in the long term. The city is known to be somewhat socioeconomically segregated, and if this is mirrored in day-cares so that the backgrounds of children in day-cares get segregated, it may also start to affect the quality of education. In this case the unevenly distributed challenges would cause institutional segregation of learning conditions in early childhood education. The institutional segregation of early childhood education or schools has not been studied much in Finland. Earlier studies on Finnish schools have been able to explain differences between schools through differences in children’s backgrounds, and there has not been a reason to doubt the institutional equality of schools’ quality. The basic principle of the Finnish early childhood education and school system is to provide every child with equal conditions and opportunities to grow and learn. These equal conditions equalise segregation in the population by offering equally high-quality education in both disadvantaged and well-off areas of the city. However, if the segregation of children’s backgrounds is accompanied by the segregation of learning conditions in day-cares, there is a risk of the cumulation of both socioeconomic disadvantage and lower quality of education. In this case, the quality would decrease exactly where it would be most needed. In my thesis I study whether there is differentiation in problems related to hiring or keeping staff in the day-cares in Helsinki, through the numbers of resigned and unqualified staff in each unit. I also look at whether this segregation of day-care units is at all related to the socioeconomic segregation of the city’s population. In the study I utilize HR data from the city of Helsinki and socioeconomic population data from Statistics Finland, which I join onto spatial data of day-cares’ locations. I use this combined dataset to study the segregation of day-cares and its connections to socioeconomic segregation using quantitative statistical methods and spatial analysis methods. The results indicate that there is perceivable segregation in the staff of day-cares in Helsinki, but socioeconomic segregation is able to statistically explain the patterns only slightly. Therefore, mostly other phenomena seem to cause the differentiation in staff related problems, but these phenomena are not yet known. In terms of institutional segregation, the early childhood education system in Helsinki seems to still be quite equal. However, more knowledge about the subject is needed, because both the results in this study, as well as previous studies show some worrying signals pointing to the possibility of institutional segregation. In addition, intense public discourse around the topic of early childhood education, and a wide-ranging worker’s strike, including day-care staff, seem demonstrative of the seriousness of these challenges in day-cares.
  • 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
  • Torkko, Jussi (Helsingin yliopisto, 2021)
    Urban greenery is vital to the people in our increasingly urbanizing societies. It is diverse in nature and provides numerous life improving qualities. Traditionally urban greenery has been assessed with a top-down view through the sensors of aerial vehicles and satellites. This does not equate on what is experienced down at the human level. An alternative viewpoint has emerged, with the introduction of a more human-scale viewpoint. To quantify this human-scale greenery, novel and disparate approaches have been developed. However, there is little knowledge on how these modelling methods and indices manage to capture the greenery we truly experience on the ground level. This thesis is an undertaking to better understand what the greenery experienced by people on the ground level, termed humanscale greenery (HSG), means. The goal was to grasp how the various modelling methods, indices and datasets can be best used to capture this phenomenon. Simultaneously, the study tries to better comprehend how different people experience greenery. To achieve this, human-scale greenery values were collected via interviews at randomly selected study sites across Helsinki. These values were then compared to modelled values at the same sites. The methods and indices tested include modern approaches developed specifically for HSG and traditional greenery assessment methods. Along the greenery values, sociodemographic variables were collected in the interviews and compared to each other in relation to HSG values. The modelled values were on average smaller than HSG values. All methods indicated very strong or strong linear relationships with human-scale greenery. NDVI and semantic segmentation Green View Index (GVI) had the strongest relationships and least deviation. Land use (LU) and color based GVI had the highest error deviations from HSG. The sociodemographic assessment showed hints that age might affect the amount of experienced greenery, but this is uncertain. With a random sampling of interviewees, 25–34-year-olds and less nature visiting people were more common at sites with low HSG. Based on the results obtained here, many different types of novel methods are suitable for modelling HSG with strong linear relationships. However, also traditional greenery assessment methods performed well. It is difficult to curtail the experience of greenery into a single approach. A solution could possibly be obtained via the combination of methods. The results also advocate the usage of machine learning methods for greenery image segmentation. These cannot be applied everywhere due to data coverage problems, but alternative methods can also be used to fill in gaps. The significance of age on the experience of greenery needs further research. Because urban greenery’s benefits are known, attention should also be given onto how different kinds of people are able to experience it. In the future we should also discuss the meaningfulness of assessing absolute greenery in comparison to the types and parts of greenery.
  • 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.
  • Rinne, Jooel (Helsingin yliopisto, 2022)
    Internet has altered the wildlife trade as it is now easy to trade animals on different online platforms. In the reptile pet trade, distinct appearance and rarity of the species are sought after attributes. Reptiles with small ranges are especially threatened by the pet trade. The Lesser Antilles are home to nearly hundred endemic lizard and snake species which are facing many threats from climate change to habitat loss. In addition, some of them are subject to international pet trade the scale of which is still not assessed properly. In this thesis I have mapped the online pet trade in endemic reptile species of the Lesser Antilles. To do this, I built an automated data collection and processing tool consisting of Python scripts. I used the tool to scrape 366 951 reptile trade advertisements from 90 distinct websites and to filter and extract information on Lesser Antillean reptiles from the collected data. The results show that most of the Lesser Antillean reptile species traded online have not yet been fully assessed for their conservation status by the International Union of the Conservation of Nature (IUCN). Overall, 9,2% off the reptiles on online sales advertisements and 21,4% of the species sold online are assessed as Threatened (i.e., at highest risk of extinction). Only 24,8% of advertisements selling Lesser Antillean reptiles online concern species that are evaluated as not Threatened by the IUCN Red List of Species. Germany was found to be the centre of trade of Lesser Antillean reptile species as the number of trade advertisements and distinct species sold was the highest there. United States was the second biggest trader of all species and the biggest trader of Convention on International Trade in Endangered Species (CITES) listed species. These results show that it is of foremost importance to evaluate the conservation status of all species that are currently traded to fully assess the threat that the pet trade possesses to reptile species. It will also be important to assess the sustainability of the reptile trade, especially in Germany and the United States. The tool used to collect and process the data in this thesis can be modified to assess the pet trade of any species on publicly accessible online platforms
  • Väisänen, Tuomas Lauri Aleksanteri; Järv, Olle; Toivonen, Tuuli; Hiippala, Tuomo (2022)
    Globalization, urbanization and international mobility have led to increasingly diverse urban populations. Compared to traditional traits for measuring urban diversity, such as ethnicity and country of origin, the role of language remains underexplored in understanding diversity, interactions between different groups and socio-spatial segregation. In this article, we analyse language use in the Helsinki Metropolitan Area by combining individual-level register data, socio-economic grid database, mobile phone and social media data to understand spatio-temporal patterns of linguistic diversity better. We measured linguistic diversity using metrics developed in the fields of ecology and information theory, and performed spatial clustering and regression analyses to explore the spatio-temporal patterns of linguistic diversity. We found spatial and temporal differences between register and social media data, show that linguistic diversity is influenced by the physical and socio-economic environment, and identified areas where different linguistic groups are likely to interact. Our results provide insights for urban planning and understanding urban diversity through linguistic information. As global urbanization, international migration and refugee flows and climate change drive diverse populations into cities, understanding urban diversity and its implications for urban planning and sustainability become increasingly important.
  • 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.
  • Laaksonen, Iivari (Helsingin yliopisto, 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 (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.