IP-adresserna under IPv4 är inte tillräckliga och har på många delar i världen redan tagit slut. IPv6 erbjuder många fler tillgängliga adresser och övergången till protokollet har redan börjat. Det Gävlebaserade företaget Interlan erbjuder en tjänst som kontrollerar om kommuner och myndigheter i de nordiska länderna är nåbara via IPv6 samt om de är säkrade med DNSSEC. Denna rapport beskriver jobbet med att förbättra och göra deras tjänst anpassningsbar för flera länder. Processen som beskrivs är parametriseringen av skripten som samlar data, skapandet av en databas att lagra data i, och ändringar till hur data presenteras så att det går att lägga till flera länder.

Denna rapport avhandlar ett arbete inom ämnet datavetenskap och handlarom utvecklingen av ett system för att studera restidkvoten för sträckor somtrafikeras av Västtrafik, som är en av Sveriges största regionalakollektivtrafikmyndigheter. Restidskvoten anges som förhållandet mellanrestiden med kollektivtrafik och att resa med personbil ([restidkollektivtrafik] / [restid biltrafik]). Med detta som grund har ett system föratt inhämta data om restider med hjälp av Google Maps Directions API samtberäkna restidskvoter och annan metadata utifrån denna insamlade dataskapats. Systemet används till att göra dagliga körningar och lagrar inhämtaddata i en databas hos Västtrafik. Till systemet finns också en dashboard somkan användas för att göra sökningar i databasen som byggts upp och på såsätt hämta, visualisera och analysera önskad data. Restidsdata förkollektivtrafik som erhållits från Google har i stickprov jämförts medrestidsdata från Västtrafik och även om det fanns några avvikelser så stämdede väl överens. Därav dras slutsatsen att data från Google går att använda tillatt beräkna och analysera restidskvoter.

The Geographic Information Systems (GIS) have become popular tool, used in different fields. The launching of Google Maps offered a new approach of building web based GIS systems; making it possible to integrate external geographically referenced data with the powerful map service supplied by Google. This thesis demonstrates the design and implementation of creating a web based geographic information system. The system is built by adapting the Google Maps API library and building a web server to display and explore agricultural data.

Målet med arbetet var att implementera grunden för en webbaserad editor till applikationen MMQ som saknar en editor för att skapa uppdrag. Genom att använda grundläggande koncept inom webb och lyssna till uppdragsgivarnas önskningar och förväntningar utvecklades en editor med grundläggande funktionalitet. Arbetet utvärderades därefter ur ett användbarhetsperspektiv med fokus på generell tillfredsställelse och igenkännlighet. Utvärderingen skedde med PSSUQ- enkäten tillsammans med några egenkonstruerade frågor i intervjuformat som ställdes testdeltagarna. Resultaten visar att den generella uppfattningen av editorn är positiv och att editorn har flertalet bekanta element som gjorde testdeltagarna trygga vid användningen. Förbättringsförslag har tagits fram från de åsikter och förslag som testdeltagarna förde fram under intervjuerna. Förslagen går i linje med uppdragsgivarnas idéer om editorn och kommer ligga till grund för en vidareutveckling av editorn till applikationen MMQ.

A presente pesquisa analisou a acurácia posicional horizontal das bases do Bing Maps, Google Maps e da World Imagery da Esri quando utilizadas como referência espacial on-line em um Sistema de Informação Geográfica no Município de São Paulo (MSP). A metodologia adotada foi a baseada no Decreto Federal no 89.817/84 e na Análise Estatística proposta por Merchant (1982). A análise da acurácia foi desenvolvida a partir das diferenças entre as coordenadas de 240 pontos nas cartas 1:1.000 do Mapa Digital da Cidade de São Paulo (MDC) em relação às coordenadas homólogas nas três bases, considerando separadamente as coordenadas do eixo Norte e Este. A base do Google Maps para o MSP foi dividida em duas (mosaico de ortofotos na área central e mosaico de imagens de satélite nas regiões periféricas), devido à grande diferença de acurácia entre os dois produtos. Para classificar cada base a partir do Decreto 89.817 foi definida a escala na qual somente 10% das discrepâncias tivessem seu valor superior ao PEC, e a escala na qual o Root Mean Square Error (RMSE) da amostra das discrepâncias fosse igual a 60,8% do PEC. A escala final selecionada foi a menor (menos detalhada) entre as definidas em cada um dos processos. A Análise Estatística foi baseada nos testes de tendência e precisão. Como as três bases apresentaram tendência, a escala definida pelo teste de precisão não foi considerada no cômputo final das escalas, devido à dificuldade de se eliminar a tendência nessas bases quando utilizadas no SIG. As escalas finais obtidas, relativas à classe A, foram: Google Maps (imagens de satélite): 1:12.400; Google Maps (ortofotos): 1:3.588; Bing Maps: 1:10.881 e Word Imagery da ESRI: 1:8.420. Concluiu-se que os três produtos com escalas próximas a 1:10.000 apresentam acurácia para serem utilizados como bases em SIGs nos estudos para planejamento urbano e que o Google Maps (ortofotos, com escala próxima a 1:4.000) pode ser igualmente utilizada para planejamento, mas em função de sua acurácia maior, pode servir também para a gestão de serviços urbanos. A principal limitação encontrada para as bases no uso como referência espacial em SIGs foi a inclinação das feições distantes do nadir da imagem ou da ortofoto e o consequente recobrimento de áreas adjacentes a essas feições. Entretanto, essa limitação se mostrou quase desprezível para as escalas definidas para as bases na análise da acurácia. / This research has analyzed the horizontal positional accuracy of basemaps Bing Maps, Google Maps and ESRIs World Imagery when used as an online spatial reference within a Geographic Information System for the municipality of São Paulo. The methodology was based on criteria defined by Brazil Federal Decree 89817/84 and in the analysis proposed by Merchant (1982). The accuracy analysis was developed observing the discrepancies between coordinates of selected 240 points from the 1:1000 digital map of São Paulo compared to corresponding points in the three basemaps, (coordinate directions North and East were considered separately). The Google Maps basemap for the city of São Paulo was divided in two (ortophoto mosaic for the central area and satellite images mosaic in the remainder peripheral areas), due to the considerable differences in their accuracy patterns. In order to classify each basemap as per Federal Decree 89.817, we have defined a scale in which only 10% of discrepancies were above the LMAS90 and the Root Mean Square Error (RMSE) of the discrepancies sample was equal to 60,8% of LMAS90. The final selected scale was the smallest (less detailed) of those obtained in each of the processes. The statistical analysis was based on the test of bias error and by a test of precision. Because the three basemaps have presented biases, the final scales defined by the precision test were not considered in the results, for it is difficult to eliminate biases in these basemaps when used in a GIS. We have obtained the following final scales to class A of the Brazilian Decree: Google Maps (area covered by satellite images): 1:12.400; Google Maps (area covered by ortophotos): 1:3.588; Bing Maps: 1:10.881 and ESRIs Word Imagery: 1:8.420. In conclusion, (a) the three products with scales around a 1:10.000 present accuracy to be used as basemap in GIS for urban planning studies and (b) Google Maps (area covered by ortophotos, scale around 1:4.000) can be equally used for planning studies, as well as urban services manager, due to its greater accuracy. The key limitations for the use of such basemaps as spatial references in GIS was the inclination of features which are distant from the image or ortophoto nadir (off-nadir effects) and the consequent shadowing of adjoining areas. However, this limitation is almost irrelevant to the scales defined for the basemaps in the accuracy analysis.

A presente pesquisa analisou a acurácia posicional horizontal das bases do Bing Maps, Google Maps e da World Imagery da Esri quando utilizadas como referência espacial on-line em um Sistema de Informação Geográfica no Município de São Paulo (MSP). A metodologia adotada foi a baseada no Decreto Federal no 89.817/84 e na Análise Estatística proposta por Merchant (1982). A análise da acurácia foi desenvolvida a partir das diferenças entre as coordenadas de 240 pontos nas cartas 1:1.000 do Mapa Digital da Cidade de São Paulo (MDC) em relação às coordenadas homólogas nas três bases, considerando separadamente as coordenadas do eixo Norte e Este. A base do Google Maps para o MSP foi dividida em duas (mosaico de ortofotos na área central e mosaico de imagens de satélite nas regiões periféricas), devido à grande diferença de acurácia entre os dois produtos. Para classificar cada base a partir do Decreto 89.817 foi definida a escala na qual somente 10% das discrepâncias tivessem seu valor superior ao PEC, e a escala na qual o Root Mean Square Error (RMSE) da amostra das discrepâncias fosse igual a 60,8% do PEC. A escala final selecionada foi a menor (menos detalhada) entre as definidas em cada um dos processos. A Análise Estatística foi baseada nos testes de tendência e precisão. Como as três bases apresentaram tendência, a escala definida pelo teste de precisão não foi considerada no cômputo final das escalas, devido à dificuldade de se eliminar a tendência nessas bases quando utilizadas no SIG. As escalas finais obtidas, relativas à classe A, foram: Google Maps (imagens de satélite): 1:12.400; Google Maps (ortofotos): 1:3.588; Bing Maps: 1:10.881 e Word Imagery da ESRI: 1:8.420. Concluiu-se que os três produtos com escalas próximas a 1:10.000 apresentam acurácia para serem utilizados como bases em SIGs nos estudos para planejamento urbano e que o Google Maps (ortofotos, com escala próxima a 1:4.000) pode ser igualmente utilizada para planejamento, mas em função de sua acurácia maior, pode servir também para a gestão de serviços urbanos. A principal limitação encontrada para as bases no uso como referência espacial em SIGs foi a inclinação das feições distantes do nadir da imagem ou da ortofoto e o consequente recobrimento de áreas adjacentes a essas feições. Entretanto, essa limitação se mostrou quase desprezível para as escalas definidas para as bases na análise da acurácia. / This research has analyzed the horizontal positional accuracy of basemaps Bing Maps, Google Maps and ESRIs World Imagery when used as an online spatial reference within a Geographic Information System for the municipality of São Paulo. The methodology was based on criteria defined by Brazil Federal Decree 89817/84 and in the analysis proposed by Merchant (1982). The accuracy analysis was developed observing the discrepancies between coordinates of selected 240 points from the 1:1000 digital map of São Paulo compared to corresponding points in the three basemaps, (coordinate directions North and East were considered separately). The Google Maps basemap for the city of São Paulo was divided in two (ortophoto mosaic for the central area and satellite images mosaic in the remainder peripheral areas), due to the considerable differences in their accuracy patterns. In order to classify each basemap as per Federal Decree 89.817, we have defined a scale in which only 10% of discrepancies were above the LMAS90 and the Root Mean Square Error (RMSE) of the discrepancies sample was equal to 60,8% of LMAS90. The final selected scale was the smallest (less detailed) of those obtained in each of the processes. The statistical analysis was based on the test of bias error and by a test of precision. Because the three basemaps have presented biases, the final scales defined by the precision test were not considered in the results, for it is difficult to eliminate biases in these basemaps when used in a GIS. We have obtained the following final scales to class A of the Brazilian Decree: Google Maps (area covered by satellite images): 1:12.400; Google Maps (area covered by ortophotos): 1:3.588; Bing Maps: 1:10.881 and ESRIs Word Imagery: 1:8.420. In conclusion, (a) the three products with scales around a 1:10.000 present accuracy to be used as basemap in GIS for urban planning studies and (b) Google Maps (area covered by ortophotos, scale around 1:4.000) can be equally used for planning studies, as well as urban services manager, due to its greater accuracy. The key limitations for the use of such basemaps as spatial references in GIS was the inclination of features which are distant from the image or ortophoto nadir (off-nadir effects) and the consequent shadowing of adjoining areas. However, this limitation is almost irrelevant to the scales defined for the basemaps in the accuracy analysis.

3 pp. / Pedometers are small devices worn at the hip to count the number of steps walked per day. Pedometers gained popularity as a tool for motivating and monitoring physical activity. The purpose of the publication was to provide basic mechanisms and functions of pedometers. Additionally, we provided information of new technology (cell phone and Google Maps) that are emerging as a tool for motivating physical activity.

Nowadays with the rapid development of cities, understanding the human mobility patterns of subscribers is crucial for urban planning and for network infrastructure deployment. Today mobile phones are electronic devices used for analyzing the mobility patterns of the subscribers in the network, because humans in their daily activities they carry mobile phones for communication purpose. For effective utilization of network infrastructure (NI) there is a need to study on mobility patterns of subscribers.   The aim of the thesis is to simulate the geospatial Telenor mobility data (i.e. three different subscriber categorized segments) and provide a visual support in google maps using google maps API, which helps in decision making to the telecommunication operators for effective utilization of network infrastructure (NI).    In this thesis there are two major objectives. Firstly, categorize the given geospatial telenor mobility data using subscriber mobility algorithm. Secondly, providing a visual support for the obtained categorized geospatial telenor mobility data in google maps using a geovisualization simulation tool.    The algorithm used to categorize the given geospatial telenor mobility data is subscriber mobility algorithm. Where this subscriber mobility algorithm categorizes the subscribers into three different segments (i.e. infrastructure stressing, medium, friendly). For validation and confirmation purpose of subscriber mobility algorithm a tetris optimization model is used. To give visual support for each categorized segments a simulation tool is developed and it displays the visualization results in google maps using Google Maps API.   The result of this thesis are presented to the above formulated objectives. By using subscriber mobility algorithm and tetris optimization model to a geospatial data set of 33,045 subscribers only 1400 subscribers are found as infrastructure stressing subscribers. To look informative, a small region (i.e. boras region) is taken to visualize the subscribers from each of the categorized segments (i.e. infrastructure stressing, medium, friendly).    The conclusion of the thesis is that the functionality thus developed contributes to knowledge discovery from geospatial data and provides visual support for decision making to telecommunication operators. Nowadays with the rapid development of cities, understanding the human mobility patterns of subscribers is crucial for urban planning and for network infrastructure deployment. Today mobile phones are electronic devices used for analyzing the mobility patterns of the subscribers in the network, because humans in their daily activities they carry mobile phones for communication purpose. For effective utilization of network infrastructure (NI) there is a need to study on mobility patterns of subscribers.   The aim of the thesis is to simulate the geospatial Telenor mobility data (i.e. three different subscriber categorized segments) and provide a visual support in google maps using google maps API, which helps in decision making to the telecommunication operators for effective utilization of network infrastructure (NI).    In this thesis there are two major objectives. Firstly, categorize the given geospatial telenor mobility data using subscriber mobility algorithm. Secondly, providing a visual support for the obtained categorized geospatial telenor mobility data in google maps using a geovisualization simulation tool.    The algorithm used to categorize the given geospatial telenor mobility data is subscriber mobility algorithm. Where this subscriber mobility algorithm categorizes the subscribers into three different segments (i.e. infrastructure stressing, medium, friendly). For validation and confirmation purpose of subscriber mobility algorithm a tetris optimization model is used. To give visual support for each categorized segments a simulation tool is developed and it displays the visualization results in google maps using Google Maps API.   The result of this thesis are presented to the above formulated objectives. By using subscriber mobility algorithm and tetris optimization model to a geospatial data set of 33,045 subscribers only 1400 subscribers are found as infrastructure stressing subscribers. To look informative, a small region (i.e. boras region) is taken to visualize the subscribers from each of the categorized segments (i.e. infrastructure stressing, medium, friendly).    The conclusion of the thesis is that the functionality thus developed contributes to knowledge discovery from geospatial data and provides visual support for decision making to telecommunication operators.

Master of Science / Department of Computing and Information Sciences / Daniel Andresen / The world is becoming smarter with an increase in efficiency of wireless communication resulting in an accelerated use of smart phones. Open source of Android market gave a chance to individuals to freely develop their own applications which could be run easily on Android smart phones. The purpose of this project is to develop an Android application for managing and tracking a user’s shopping information. The Smart tracker is about tracking the user's purchase of clothes and accessories. The main advantage of this Android app is that it helps to remember the size of clothing purchased. When a user shops at a retail store, he feeds the information pertaining to the fitting of the clothes purchased brand wise, whether it is a correct fit or a bit loose/tight. The data thus entered can be retrieved when he decides to make a purchase in the same store in future. This tremendously decreases the user’s shopping time and makes the experience easier as the right size is already known and hence there is no hassle of using trial rooms to see if the clothes fit. In addition to size, Smart tracker also stores the price at which the item was bought and any additional user comments along with the store location. When data pertaining to a purchased clothing item is entered, the app provides the option to append an image of the item either by capturing a picture through the smart phone’s camera or by uploading an image from the gallery. This helps the user recount the item to which the information corresponds. Another important feature is that tracking of purchases for a family can be personalized, i.e., the shopping information of each family member can be stored separately (especially for kids who can't track their own expense). Additionally, the user can also keep track of money spent on each item/brand/family member. Smart tracker also enables user to create a wish list to remember what they (or a friend) liked but didn't buy in a particular store.

Master of Science / Department of Computing and Information Sciences / Daniel A. Andresen / The purpose of this project is to develop an Android application for Fort Riley soldiers so as to support the theme “War Fighters Supporting War Fighters by Caring for Military Training Lands”. There are four core features in this application: 1. Find a Gully 2. Report a Gully 3. View Current Weather 4. View Current Satellite Image Features are explained in brief below. 1. Find a gully In this feature based on the current location of soldier, application will display all gullies near that location using Google Maps API. Soldier can also view the gully details by tapping the gully icon. 2. Report a Gully In this feature, soldier can report a new gully i.e.; gully which is not already present on the map. This gully will be stored as unverified gully in the database. Once this gully is verified it will be changed to verified gully and it will be plotted on the Google map. 3. View Current Weather In this feature, soldier can view the current weather conditions of Fort Riley. 4. View Current Satellite Image In this feature, soldier can view the current satellite image of Fort Riley.