Browsing by Subject "GIS"

Työssä tutkitaan paikkatietojärjestelmiä (GIS) käyttäen Helsingin, Espoon ja Vantaan palo- ja pelastustoimen yhteistoimintasopimusta. Samalla tutkitaan paikkatietojärjestelmien soveltuvuutta palo- ja pelastustoimen yhteistoiminnan suunnitteluun. Palo- ja pelastustoimilain mukaisesti kuntien tulee mitoittaa palo- ja pelastustoimen valmiutensa siten, että se on riittävä. Arviointia varten kunnat on jaettu riskialueisiin I-IV, jossa I on korkeariskisin alue. Sisäasiainministeriön pelastusosaston ohjeen mukaan pelastustoiminnan tulisi käynnistyä I-riskialueella kuuden minuutin, II-riskialueella kymmenen minuutin ja III-riskialueella 20 minuutin kuluttua hälytyksestä. IV-riskialueella aika voi olla pitempi kuin I-IV -riskialueilla. Helsingin, Espoon ja Vantaan palo- ja pelastustoimien välillä solmittiin 2.2.1995 yhteistoimintasopimus, jonka mukaan yhteistoiminta-alueella tapahtuviin hälytystehtäviin lähetetään kuntarajoista riippumatta lähin yksikkö. Tutkielma pohjautuu tähän sopimukseen. MapInfo-paikkatieto-ohjelmaa käyttäen piirretään Helsingin, Espoon ja Vantaan paloyksiköiden saavutettavuusajoista saman ajan käyriä eli isokroneja, sekä vertaillaan niitä riskialueluokitukseen. Oleellista on tunnistaa ne alueet joita ei tavoiteta riskialueluokituksen edellyttämässä suositusajassa, sekä ne alueet jonne naapurikaupungin paloyksikkö ehtii omaa yksikköä nopeammin. Tutkimuksen mukaan Helsingin maapinta-alasta 3 % I-riskiluokkaan kuuluvia alueita ja 3 % II-riskiluokkaan kuuluvia alueita jää tavoittamatta riskialueluokituksen edellyttämässä ajassa. Vastaavat luvut Espoossa ovat 3 % ja 7 %, sekä Vantaalla 17 % ja 1 %. Yhteistoimintasopimuksen puitteissa toisen kaupungin paloyksikötkin tavoittavat suositusajassa vain hyvin pienen osan näistä alueista. Poikkeus on Vantaan antama apu Pohjois-Espooseen. Vantaan yksiköt tavoittavat suositusajassa 13 km2 Espoon II-riskialueista. Luku on 4 % Espoon maapinta-alasta. Tutkimuksessa tehdään ehdotus alueista jotka kannattaisi hoitaa toisin kuin voimassa oleva yhteistoimintasopimus edellyttää. Mukana on myös kartta, jossa kuntarajat on piirretty paloyksiköiden saavutettavuusaikojen mukaan. Kartassa kukin kaupunki muodostuu siitä alueesta, jonne kaupungin paloyksiköt ehtivät ensimmäisenä kolmesta kaupungista. Tutkimuksessa käy ilmi, että käytetyt työmetodit, ohjelmistot ja aineisto soveltuvat sellaisenaan minkä tahansa kunnan palo- ja pelastustoimen suunnitteluun ja yhteistoiminnan arviointiin. Tarvetta jatkotutkimuksiin olisi ainakin yhteistoiminta-alueen laajentamisesta, sekä riskianalyysien ja sairaankuljetusyksiköiden sijoittelun tekemisestä paikkatietojärjestelmiä apuna käyttäen.

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

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

Vuodesta 2010 alkaen syntyvyys on laskenut Suomessa yhdeksän vuotta peräjälkeen. Väestön heikko uusiutuminen on nostanut julkisuudessa esiin huolia hyvinvointivaltion ylläpidosta ja tulevien eläkkeiden maksukyvystä. Tutkielmassa selvitetään, miten sosioekonomisten tekijöiden muutokset Suomessa vaikuttavat kuntakohtaiseen kokonaishedelmällisyyteen. Työttömyyden, koulutusasteen, opiskelijoiden suhteellisen osuuden, muuttoliikkeiden, sekularisaation ja tulotason vaikutusta kokonaishedelmällisyyslukemiin tarkastellaan erilaisin tilastollisin menetelmin. Tuloksia verrataan aiempaan hedelmällisyystutkimukseen ja esitetään alueellisia erityispiirteitä hedelmällisyyskäyttäytymisen saralta. Aineisto koostuu Tilastokeskuksen laatimista väestötilastoista, joiden avulla kunnittaiset kokonaishedelmällisyystilastot on laskettu vuosille 1987–2017. Kuntakohtaisia kokonaishedelmällisyyslukuja verrataan Tilastokeskuksen laatimiin sosioekonomisiin muuttujiin ja havaintoja vertaillaan toisiinsa. Hyödynnettävä tutkimusmenetelmä on kiinteiden vaikutusten malli paneeliaineistolla, jossa kuntia vertaillaan sekä väestöpainotuksella että ilman. Lisäksi hedelmällisyystilastoja tarkastellaan maantieteellisesti painotetun regression avulla, jolloin alueelliset painopisteet ja maantieteelliset erot tulevat näkyviin. Kiinteiden vaikutusten mallista ilmenee, että työttömyyden, opiskelijoiden osuuden, sekularisaation ja tuloluokista varakkaimman kymmenyksen suhteellisen väestömäärän kasvu kunnissa vaikuttaa negatiivisesti kokonaishedelmällisyyteen. Toisen asteen ja korkea-asteisen koulutuksen yleistyminen sekä kunnan muuttovoitto nostavat kunnittaisia kokonaishedelmällisyyslukemia. Maantieteellisesti painotettujen regressiotulosten perusteella Suomesta löytyy varsin erilaisia hedelmällisyyskäyttäytymisen alueita. Käytetyt sosioekonomiset muuttujat ennustavat toteutunutta kunnittaista kokonaishedelmällisyyttä hyvin Pohjanmaan, Keski-Suomen ja Kainuun alueilla. Etelä-Suomessa sosioekonomiset muuttujat ennustavat toteutunutta kokonaishedelmällisyyttä heikosti. Poikkeuksena tästä on opiskelijoiden suhteellinen osuus, joka kasvaessaan ennustaa tarkastelun kohteena olevan kunnan heikentyvää kokonaishedelmällisyyttä merkittävästi etenkin Etelä-Suomen alueella. Kokonaishedelmällisyyden ja sosioekonomisten tekijöiden välisiä mekanismeja vertailtaessa yhteiskunnan rakenteelliset erot eri alueiden välillä nousevat esiin.

Lack of physical activity and obesity are increasing problems that have caused higher healthcare expenses for society. As prior studies have shown, there is a connection between proximity to a sports facility and increased physical activity. Public sports facilities are a way of preventing segregation by providing opportunities for recreational sports for everybody. In my thesis, I studied spatial segregation and accessibility to swimming halls in the Greater Helsinki region. Spatial segregation was studied in terms of travel times to the nearest swimming hall between the most advantaged and the most disadvantaged areas. The disadvantage sum index was used to identify the most advantaged and the most disadvantaged areas which were classified into quintiles by the index. The study was conducted using open source GIS data and applications apart from segregation analysis. Travel times to the nearest swimming facility were calculated using Helsinki Region Travel Time Matrix (250m x 250m grid). Travel times were calculated for six different types of transportation: walking, cycling, public transportation (rush hour and midday) and private cars (rush hour and midday). Statistically significant differences between the most advantaged and the most disadvantaged quintiles were calculated with Student’s t-test in SPSS. The analysis showed that spatial accessibility to swimming halls in the Greater Helsinki region is generally good. Swimming halls have by far the best accessibility by cycling and private car. Travel times to the nearest swimming halls were shorter with all types of transportation for the most disadvantaged than the most advantaged which indicates that living in a more deprived area does not restrict spatial accessibility to swimming halls.

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.

I choose to study Pääjärvi catchment area in South Finland, since different researches made at Lammi Bilogical Station are connected to my topic. I studied scientific papers from the area, especiall PRO-DOC project, collected basic data about the area and made my own research with my own approach. I used measured and online data as well meanwhile I created my own GIS maps connected to landscape ecological approach. I picked the most important landscape factors (elevation, slope, aspect, bedrock, soil, site type) and tested their correlation to each other and forest biomass and soil carbon stocks. As results I got that forest ecosystems are very complicated, each factor has impact on others, but only site type had stronger relations to every factor, especiall to both carbon stocks. But the topic requires more research.

Climate change and biodiversity loss are among the two most serious environmental issues humanity is currently facing. One way of mitigating climate change is to build more wind energy. In Finland, upcoming wind farms are going to increase the national wind energy capacity by almost tenfold. As more wind farms are built, helping in climate change mitigation, the negative biodiversity impacts caused by wind turbines are also increasing. Negative biodiversity effects caused by wind energy include habitat loss, avian mortalities, habitat fragmentation and avoidance behaviour in wildlife. This conflict where two desirable environmental goals have negative counter-effect on each other can be called green-green dilemma. This thesis looks at the biodiversity impacts on habitats caused by wind farms in Finland, and what would be the scale of a habitat tax paid for displacing natural habitat, that would help solve the green-green dilemma. This thesis utilizes geographical information system data of upcoming and in production wind farms and habitats to figure out which habitats are displaced by wind farms in Finland. Also, a wind farm level cost-benefit analysis was done for wind farms in production determine a scale of taxes, which would make 10 % or 25 % of wind farms with lowest net present value compared to habitat impact non-profitable. Two kinds of taxes were considered. Tax based on the quantity of habitat displaced, and a tax based on the quality of habitat displaced. For the determination of the quality of habitat, European red list of habitats was utilized in creation of a prioritization system for different habitats based on their endangerment category. With the prioritization system, each wind farm was given habitat points based on the habitats it was displacing. According to the results of the thesis, wind farms in Finland are mostly displacing woodland habitats. The second most common habitat displaced was marine habitats and the third most common were mires, bogs and fens. According to the prioritization system created for this thesis, most habitats displaced by wind farms are not considered threatened. Still, there should be some consideration about the habitats displaced by wind farms, as minority of habitats were considered threatened according to the prioritization system. Also, we cannot draw too many conclusions about the status of the habitats displaced as the prioritization system has flaws. The two different taxes looked in this thesis both ended up making mostly the same wind farms non-profitable, meaning there were outlier wind farms with low benefits with relatively high habitat impacts. Quantity of habitats-based tax which made 10 % of the wind farms non-profitable was 1.6 million euros per hectare of displaced habitat, and the higher tax rate making 25 % of the wind farms non-profitable was 2.5 million euros per hectare. The habitat quality-based tax was 510,000 € per habitat point for lower rate, and 750,000 € per habitat point for the higher rate. On average, quality tax in Finnish wind farms would be 1.75 million euros with the lower rate per hectare of habitat displaced, and 2.3 million euros per hectare with the higher rate according to the calculations in this thesis. Habitat tax can be one solution for solving the green-green dilemma. Taxes presented in this thesis are considerable higher than habitat restoration costs estimated for Finland, which are approximately between 8000 € and 15000 € per hectare, depending on the habitat restored. Still, a habitat tax needs to be high enough to have an impact on the economic decision making of wind farm developers. If a tax habitat tax would be implemented, it would be best to think about the desired effect of the tax, which will affect the scale of the tax. Also, all kinds of activities displacing natural habitat should be included in the tax, not just displacement caused by wind farms for the tax to be more comprehensive.

Hirvieläinonnettomuudet ovat merkittävä riskitekijä Suomen tieliikenteessä. Hirvieläinonnettomuuksissa on 2000-luvulla kuollut vuosittain kesimäärin 6 ja loukkaantunut 218 ihmistä. Merkittävistä taloudellista ja yhteiskunnallisista kustannuksista huolimatta Suomessa on tehty vain vähän tutkimusta hirvikolareihin vaikuttavista tekijöistä. Maisematekijöiden vaikutusta hirvikolariskiin ei ole toistaiseksi tutkittu lainkaan. Jotta hirvikolareita pystyttäisiin ennaltaehkäisemään nykyistä tehokkaammin, tarvitaan aiempaa parempia ja tarkempia keinoja tunnistaa riskialteimmat tieosuudet ja jopa yksittäiset paikat, joissa kolarit ovat todennäköisimpiä. Tutkielman tavoitteena oli selvittää miten eri ympäristömuuttujat vaikuttavat hirvikolareiden tapahtumisen todennäköisyyteen. Lisäksi pohdittiin, voidaanko ympäristömuuttujien tunnistamisen perusteella kehittää kolareiden ennaltaehkäisymenetelmiä. Tutkimusta varten kerättiin suurriistavirka-apu -henkilöiltä 218 kolaripaikkaa Keski-Suomen, Pohjois- ja Etelä-Savon Riistakeskuksen aluetoimistojen alueilta. SRVA-henkilöt käyvät jokaisella kolaripaikalla. He ovat paikallisia metsästäjiä, joten heidän paikallistuntemuksensa on erinomainen ja kolaripaikat ovat tarkasti tiedossa. Tutkielmassa tarkasteltiin kolari- ja kontrollipaikkojen (ei kolaria) eroja kahdessa eri mittakaavassa, makro- ja mikrotasolla. Makrotason tarkastelussa ympäristöä tutkittiin 1 000 metriä halkaisijaltaan olevan ympyrän sisältä, mikrotasolla ympyrän halkaisija oli 200 metriä. Ympäristömuuttujien vaikutusta hirvikolarin tapahtumisen todennäköisyyteen selvitettiin logistisella regressioanalyysillä. Logistisesta regressiosta käytettiin muunnosta ”matched logistic regression -analysis”, jossa jokaiselle kolaripaikalle valitaan yksilöllinen kontrollipaikka. Hirvikolaririskiin vaikuttavien muuttujien merkitystä tutkittiin menetelmällä, jossa muuttujajoukosta valittiin kerrallaan yksi muuttuja, jonka vetosuhteen (OR) muutosta tulkittiin vakioimalla muita muuttujia. Makrotason tarkastelussa kolarit keskittyivät tien metsäisille osuuksille ja puulaji oli useimmiten kuusi tai koivu kuin mänty. Kolaripaikoilla oli enemmän varttunutta ja nuorta metsää kuin kontrollipaikoilla. Kolaripaikkojen maisema sisälsi vähemmän maataloutta, vesistöjä ja rakennettua aluetta kuin kontrollipaikkojen. Mikrotasolla tutkittiin metsänrakenteen lisäksi kolari- ja kontrollipaikkojen sijaintien välisiä eroja ja maastonmuotoja. Kolaripaikat sijaitsivat lähempänä metsänreunaa ja kauempana asutusta kuin kontrollipaikat. Kolaritpaikat olivat hieman keskimääräistä maastoa korkeammalla kuin kontrollipaikat. Kolaripaikoilla oli enemmän varttunutta tai nuorta kasvatusmetsää ja puulaji oli useammin kuusi tai koivu kuin mänty. Tulosten perusteella voidaan päätellä, että kolaririski vaihtelee suuresti tien eri osuuksilla. Erityisesti metsän reunalla oli muista muuttujista riippumaton vaikutus kolaririskiin, joten ne ovat potentiaalisia ns. ”hot spotteja”. Jos riskialttiit paikat voitaisiin tunnistaa nykyistä tarkemmin, jopa 100–200 metrin tarkkuudella, hirvivaarasta varoittavia liikennemerkkejä voitaisiin kehittää, esimerkiksi lyhentämällä varoitusaluetta ja lisäämällä nopeusrajoituksia kohdennetusti. Tehokkain ja halvin keino vähentää hirvikolareita on alentaa kulkuneuvon nopeutta riskialteimmissa paikoissa.