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The overall aim of this thesis was to develop and evaluate new knowledge- and data-driven methods for supporting treatment and providing information for better assessment of Parkinson’s disease (PD).

PD is complex and progressive. There is a large amount of inter- and intravariability in motor symptoms of patients with PD (PwPD). The current evaluation of motor symptoms that are done at clinics by using clinical rating scales is limited and provides only part of the health status of PwPD. An accurate and clinically approved assessment of PD is required using frequent evaluation of symptoms.

To investigate the problem areas, the thesis adopted the microdata analysis approach including the stages of data collection, data processing, data analysis, and data interpretation. Sensor systems including smartphone and tri-axial motion sensors were used to collect data from advanced PwPD experimenting with repeated tests during a day. The experiments were rated by clinical experts. The data from sensors and the clinical evaluations were processed and used in subsequent analysis.

The first three papers in this thesis report the results from the investigation, verification, and development of knowledge- and data-driven methods for quantifying the dexterity in PD. The smartphone-based data collected from spiral drawing and alternate tapping tests were used for the analysis. The results from the development of a smartphone-based data-driven method can be used for measuring treatment-related changes in PwPD. Results from investigation and verification of an approximate entropy-based method showed good responsiveness and test-retest reliability indicating that this method is useful in measuring upper limb temporal irregularity.

The next two papers, report the results from the investigation and development of motion sensor-based knowledge- and data-driven methods for quantification of the motor states in PD. The motion data were collected from experiments such as leg agility, walking, and rapid alternating movements of hands. High convergence validity resulted from using motion sensors during leg agility tests. The results of the fusion of sensor data gathered during multiple motor tests were promising and led to valid, reliable and responsive objective measures of PD motor symptoms.

Results in the last paper investigating the feasibility of using the Dynamic Time-Warping method for assessment of PD motor states showed it is feasible to use this method for extracting features to be used in automatic scoring of PD motor states.

The findings from the knowledge- and data-driven methodology in this thesis can be used in the development of systems for follow up of the effects of treatment and individualized treatments in PD.

This thesis consists of four research papers presenting a microdata analysis approach to assess and evaluate the Parkinson’s disease (PD) motor symptoms using smartphone-based systems. PD is a progressive neurological disorder that is characterized by motor symptoms. It is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Both patients’ perception regarding common symptom and their motor function need to be related to the repeated and time-stamped assessment; with this, the full extent of patient’s condition could be revealed. The smartphone enables and facilitates the remote, long-term and repeated assessment of PD symptoms. Two types of collected data from smartphone were used, one during a three year, and another during one-day clinical study. The data were collected from series of tests consisting of tapping and spiral motor tests. During the second time scale data collection, along smartphone-based measurements patients were video recorded while performing standardized motor tasks according to Unified Parkinson’s disease rating scales (UPDRS).

At first, the objective of this thesis was to elaborate the state of the art, sensor systems, and measures that were used to detect, assess and quantify the four cardinal and dyskinetic motor symptoms. This was done through a review study. The review showed that smartphones as the new generation of sensing devices are preferred since they are considered as part of patients’ daily accessories, they are available and they include high-resolution activity data. Smartphones can capture important measures such as forces, acceleration and radial displacements that are useful for assessing PD motor symptoms.

Through the obtained insights from the review study, the second objective of this thesis was to investigate whether a combination of tapping and spiral drawing tests could be useful to quantify dexterity in PD. More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. The results from this study showed that tapping and spiral drawing tests that were collected by smartphone can detect movements reasonably well related to under- and over-medication.

The thesis continued by developing an Approximate Entropy (ApEn)-based method, which aimed to measure the amount of temporal irregularity during spiral drawing tests. One of the disabilities associated with PD is the impaired ability to accurately time movements. The increase in timing variability among patients when compared to healthy subjects, suggests that the Basal Ganglia (BG) has a role in interval timing. ApEn method was used to measure temporal irregularity score (TIS) which could significantly differentiate the healthy subjects and patients at different stages of the disease. This method was compared to two other methods which were used to measure the overall drawing impairment and shakiness. TIS had better reliability and responsiveness compared to the other methods. However, in contrast to other methods, the mean scores of the ApEn-based method improved significantly during a 3-year clinical study, indicating a possible impact of pathological BG oscillations in temporal control during spiral drawing tasks. In addition, due to the data collection scheme, the study was limited to have no gold standard for validating the TIS. However, the study continued to further investigate the findings using another screen resolution, new dataset, new patient groups, and for shorter term measurements. The new dataset included the clinical assessments of patients while they performed tests according to UPDRS. The results of this study confirmed the findings in the previous study. Further investigation when assessing the correlation of TIS to clinical ratings showed the amount of temporal irregularity present in the spiral drawing cannot be detected during clinical assessment since TIS is an upper limb high frequency-based measure. 

Parkinson’s disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.

Title: Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors

Objective: To develop and evaluate machine learning methods for assessment of Parkinson’s disease (PD) motor symptoms using leg agility (LA) data collected with motion sensors during a single dose experiment.

Background: Nineteen advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were recruited in a single center, open label, single dose clinical trial in Sweden [1].

Methods: The patients performed up to 15 LA tasks while wearing motions sensors on their foot ankle. They performed tests at pre-defined time points starting from baseline, at the time they received a morning dose (150% of their levodopa equivalent morning dose), and at follow-up time points until the medication wore off. The patients were video recorded while performing the motor tasks. and three movement disorder experts rated the observed motor symptoms using 4 items from the Unified PD Rating Scale (UPDRS) motor section including UPDRS #26 (leg agility), UPDRS #27 (Arising from chair), UPDRS #29 (Gait), UPDRS #31 (Body Bradykinesia and Hypokinesia), and dyskinesia scale. In addition, they rated the overall mobility of the patients using Treatment Response Scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). Sensors data were processed and their quantitative measures were used to develop machine learning methods, which mapped them to the mean ratings of the three raters. The quality of measurements of the machine learning methods was assessed by convergence validity, test-retest reliability and sensitivity to treatment.

Results: Results from the 10-fold cross validation showed good convergent validity of the machine learning methods (Support Vector Machines, SVM) with correlation coefficients of 0.81 for TRS, 0.78 for UPDRS #26, 0.69 for UPDRS #27, 0.78 for UPDRS #29, 0.83 for UPDRS #31, and 0.67 for dyskinesia scale (P<0.001). There were good correlations between scores produced by the methods during the first (baseline) and second tests with coefficients ranging from 0.58 to 0.96, indicating good test-retest reliability. The machine learning methods had lower sensitivity than mean clinical ratings (Figure. 1).

Conclusions: The presented methodology was able to assess motor symptoms in PD well, comparable to movement disorder experts. The leg agility test did not reflect treatment related changes.

The aim of this paper is to develop and evaluate a multi-sensor data fusion platform for quantifying Parkinson’s disease (PD) motor states. More specifically, the aim is to evaluate the clinimetric properties (validity, reliability, and responsiveness to treatment) of the method, using data from motion sensors during lower- and upper-limb tests.

Methods: Nineteen PD patients and 22 healthy controls were recruited in a single center study. Subjects performed standardized motor tasks of Unified PD Rating Scale (UPDRS), including leg agility, hand rotation, and walking after wearing motion sensors on ankles and wrists. PD patients received a single levodopa dose before and at follow-up time points after the dose administration. Patients were video recorded and their motor symptoms were rated by three movement disorder experts. Experts rated each and every test occasions based on the six items of UPDRS-III (motor section), the treatment response scale (TRS) and the dyskinesia score. Spatiotemporal features were extracted from the sensor data. Features from lower limbs and upper limbs were fused. Feature selection methods of stepwise regression (SR), Lasso regression and principle component analysis (PCA) were used to select the most important features. Different machine learning methods of linear regression (LR), decision trees, and support vector machines were examined and their clinimetric properties were assessed.

Results: Treatment response index from multimodal motion sensors (TRIMMS) scores obtained from the most valid method of LR when using data from all tests. Features were selected by SR, and this method resulted in r=0.95 to TRS. The test-retest reliability of TRIMMS was good with intra-class correlation coefficient of 0.82. Responsiveness of the TRIMMS to levodopa treatment was similar to the responsiveness of TRS.

Conclusions: The results from this study indicate that fusing motion sensors data gathered during standardized motor tasks leads to valid, reliable and sensitive objective measurements of PD motor symptoms. These measurements could be further utilized in studies for individualized optimization of treatments in PD.

Title: Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms

Objective: To assess the feasibility of measuring Parkinson’s disease (PD) motor symptoms with a multi-sensor data fusion method. More specifically, the aim is to assess validity, reliability and sensitivity to treatment of the methods.

Background: Data from 19 advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were collected in a single center, open label, single dose clinical trial in Sweden [1].

Methods: The patients performed leg agility and 2-5 meter straight walking tests while wearing motion sensors on their limbs. They performed the tests at baseline, at the time they received the morning dose, and at pre-specified time points until the medication wore off. While performing the tests the patients were video recorded. The videos were observed by three movement disorder specialists who rated the symptoms using a treatment response scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). The sensor data consisted of lower limb data during leg agility, upper limb data during walking, and lower limb data during walking. Time series analysis was performed on the raw sensor data extracted from 17 patients to derive a set of quantitative measures, which were then used during machine learning to be mapped to mean ratings of the three raters on the TRS scale. Combinations of data were tested during the machine learning procedure.

Results: Using data from both tests, the Support Vector Machines (SVM) could predict the motor states of the patients on the TRS scale with a good agreement in relation to the mean ratings of the three raters (correlation coefficient = 0.92, root mean square error = 0.42, p<0.001). Additionally, there was good test-retest reliability of the SVM scores during baseline and second tests with intraclass-correlation coefficient of 0.84. Sensitivity to treatment for SVM was good (Figure 1), indicating its ability to detect changes in motor symptoms. The upper limb data during walking was more informative than lower limb data during walking since SVMs had higher correlation coefficient to mean ratings.  

Conclusions: The methodology demonstrates good validity, reliability, and sensitivity to treatment. This indicates that it could be useful for individualized optimization of treatments among PD patients, leading to an improvement in health-related quality of life.

The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson’s disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD.

Parkinson's disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.

Objectives: The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson’s disease (PD). More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. Methods: Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests using a smartphone. Patients performed the tests before, and at pre-specified time points after they received 150% of their usual levodopa morning dose. Patients were video recorded and their motor symptoms were assessed by three movement disorder specialists using three Unified PD Rating Scale (UPDRS) motor items from part III, the dyskinesia scoring and the treatment response scale (TRS). The raw tapping and spiral data were processed and analyzed with time series analysis techniques to extract 37 spatiotemporal features. For each of the five scales, separate machine learning models were built and tested by using principal components of the features as predictors and mean ratings of the three specialists as target variables. Results: There were weak to moderate correlations between smartphone-based scores and mean ratings of UPDRS item #23 (0.52; finger tapping), UPDRS #25 (0.47; rapid alternating movements of hands), UPDRS #31 (0.57; body bradykinesia and hypokinesia), sum of the three UPDRS items (0.46), dyskinesia (0.64), and TRS (0.59). When assessing the test-retest reliability of the scores it was found that, in general, the clinical scores had better test-retest reliability than the smartphone-based scores. Only the smartphone-based predicted scores on the TRS and dyskinesia scales had good repeatability with intra-class correlation coefficients of 0.51 and 0.84, respectively. Clinician-based scores had higher effect sizes than smartphone-based scores indicating a better responsiveness in detecting changes in relation to treatment interventions. However, the first principal component of the 37 features was able to capture changes throughout the levodopa cycle and had trends similar to the clinical TRS and dyskinesia scales. Smartphone-based scores differed significantly between patients and healthy controls. Conclusions: Quantifying PD motor symptoms via instrumented, dexterity tests employed in a smartphone is feasible and data from such tests can also be used for measuring treatment-related changes in patients.

AIM: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks.

METHOD: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients' videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS.

RESULTS: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89.

CONCLUSION: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.

Parking a vehicle can often lead to frustration, air pollution and congestion due to limited availability of parking spaces. With increasing population density this problem can certainly increase unless addressed. Parking lots occupy large areas of scarce land resource therefore it is necessary to identify the driving behaviour in a parking lot to improve it further. This Paper tries study the driving behaviour in the parking lot and for this endeavours it conducted direct observation in three parking lots and used GPS data that was collected prior to this study by the University of Dalarna.

To evaluate the driving behaviour in the parking lot direct observation was conducted to obtain overall indices of the parking lot vehicles movement. The parking route taken by the driver was compared with the optimal path to identify the driving behaviour in parking lot in terms of distance. The collected data was evaluated, filtered and analysed to identify the route, the distance and the time the vehicle takes to find a parking space.

The outcome of the study shows that driving behaviour in the parking lots varies significantly among the parking user where most of the observed vehicles took unnecessary long time to complete their parking. The study shows that 56% of the 430 observed vehicles demonstrated inefficient driving behaviour as they took long driving path rather the than the optimal path. The study trace this behaviour to two factors, first, the absent of parking guidance in the parking lots and the second is the selectivity of the drivers when choosing the parking space.

The study also shows that the ability of GPS data to identify the driving behaviour in the parking lots varies based on the time interval and the type of the device that is being used. The small the time interval the more accurate the GPS data in detecting the driving behaviour in the parking lots.

The mobility of people, freight and information is fundamental to economic and social activities such as commuting, manufacturing, distributing consumer goods and supplying energy. There are two major problems that arise as a result of mobility. The first is economic cost and the second is environmental impact which is of increasing concern in sustainable development due to emission levels, particularly as a result of car use. This study focuses on constructing a network of induced emissions (NOIEs) by using three models and checking the robustness of NOIEs under varying parameters and models. The three models are Stead’s model, the NAEI model, and Oguchi’s model. This study uses the Swedish city of Borlänge as the case study.

Calculating CO2 emissions by constructing the NOIEs using Stead’s model appears to give an underestimation when compared to results from a NOIEs which applies Oguchi’s model. Results when applying the NAEI model in constructing a NOIEs also give an underestimation compared to a NOIEs applying Oguchi’s model. Applying the NAEI model is, however, more accurate than applying Stead’s model in constructing a NOIEs.

The outcomes of this study show that constructing a NOIEs is robust using Oguchi’s model. This model is preferable since it takes into account more important variables such as driving behavior and the length of the road segments which have a significant impact when estimating CO2 emissions.

Indoor air quality is a measure of clean air with comfort conditions and depiction of lower concentration of air pollutants. It is tedious task to achieve all quality measures at a time with smart energy consumption. This research aims to come up with a solution of how to improve smart ventilation system in order to get clean indoor air with less consumption of electric energy. Many studies showed that scheduled ventilation system has proven to be a good solution to this problem. For this purpose, a long-term sensor data of smart ventilation system Renson healthbox and Luvians data is studied which is operated in Dalarnas villa. This research investigates how this system works in two modes and to improve it by customized scheduling.A regression model is constructed in which the relationship between airflow and CO2 is shown. For this purpose, correlation analysis is used in which the connection of bonds between each data features are analyzed. After the feature selection, as a result from correlation matrix, regression analysis is used to find out whether the selected features are linearly related or not. Regression analysis also used for the intent to quantify a model to estimate the flowrate and CO2. A mathematical model is also build to simulate the flowrate and CO2 with energy consumption.The results showed that, in order to provide better indoor air quality with efficient energy consumption, a necessary modification of the fan schedule should be done in a way that fan must be started little bit earlier to avoid harmful particles reach their upper threshold limits. This can result in reduction of fan’s maximum speed hence consumption of less energy is achieved.

Extracting knowledge from user-generated content (UGC) in social media platforms is a very hot research topic in the area of machine learning, nonetheless, the main challenge resides in the fact that UGC carries inference, abstraction and subjectivity alongside objectivity. With the aim of recognising the importance of subjectivity as an influential aspect for providing humanoid results from a machine learning algorithm, this study proposes a novel approach to improve Instagram hashtag recommendation by considering sentiment that can be expressed for images. Two main points are studied in this thesis; evaluating the relativity of Instagram image to hashtag for both objective and subjective features of an image and the effect of sentiment on said relativity. This work examines three machine learning methods for hashtag recommendation: AWS service, developed algorithms with and without sentiment considerations. The models are tested on a collected dataset of de-identified Instagram posts in location London gathered from public profiles. The results show that considering sentiment significantly improves Instagram hashtag recommendation.

It would be of both patients’ as well as clinicians’ interest, if diagnosis of Parkinson’s disease (PD) as well as following check-up methods were perfectly sensitive, accurate, reproducible and feasible of objectively classifying motor symptoms of PD. This is an arduous task due to the possible subjectivity of clinical evaluations. In the past decade, attention turns into a multitude of technology based measures (TBMs) to address this need, among which the method of this research is positioned. Author hopes to contribute with a motor assessment method that addresses not only the issue of subjectivity of measurement, but also does not require extensive installments and is easy to use. For this study, data from a clinical trial conducted at Uppsala University Hospital, Sweden in 2015 are used. 7 PD patients and 7 healthy controls each performed 7-13 times each the same motoric gait test, which has been was video recorded. These recordings were showed to clinicians, who rated subjects’ gait and possible dyskinesia on the unified Parkinson's disease rating scale (0-4 rating). Thus the aim of this research was to imitate and automate the tasks of clinicians when diagnosing PD and its symptoms through motoric ratings, using various gait features. These gait features were obtained through quantification of signals from different body parts while patient performs walking motoric test, using image processing. Diagnosis of PD and its symptoms was twofold, as to firstly identify whether the subject has PD and to secondly predict the severity of PD patients symptoms. When classifying subjects into healthy controls and PD patients, classification trees and support vector machines have been deployed, while these achieved 76- 85% accuracy depending on features selected. Following focus was to diagnose severity of PD among patients, while using UPDRS ratings by clinicians as a target variable for supervised learning. Herein, linear regression has been deployed, while average absolute prediction error was 0.25 and correlation of UPDRS ratings with predicted values was 0.84.

As the development of hardware, the standardisation of threat data formats and the availability of high-quality free data streams proceeds it enables organisations to manage more substantial quantities of threat data. Making intelligent use of this data is complex and platforms which offers threat data management platforms are often expensive. This study aims to explore tools, frameworks and taxonomies which can aid an organisation looking to implement a threat intelligence platform of their own. Available open source frameworks, tools and data sources are compared and analysed. By using the design and creation research methodology, a conceptualised system is designed and proposed. The study is of use for anyone looking to gain a fundamental understanding of the currently available tools, frameworks and taxonomies relevant to the field of threat intelligence.

Today, millions of websites that can be considered web applications exist. When developing a web application, following a design pattern can make a big difference. MVC, short for Model-View-Controller is a popular design pattern that is used by many frameworks for web development. When developing web applications, choosing the right framework is important. The two most common technologies used in websites are PHP and ASP.NET. In this study, the PHP framework CodeIgniter was compared with the framework ASP.NET Core MVC. The purpose of the study was to compare the frameworks with respect to their working environment, developing language, and connection to database, as well as comparing their performance. To do this, the research strategy design and creation was used. Two prototypes of the same web application were developed, one using CodeIgniter and the other using ASP.NET Core MVC. These prototypes were developed in the form of an e-shop website and were used to compare the two frameworks. During the development of the prototypes, data relating to the three categories working environment, developing language, and connection to database was collected from the official documentation and from the development itself. The performance of the prototypes was compared by doing a load test with the performance testing tool Apache JMeter. The results showed that CodeIgniter was easier to get started with and provided more options when setting up the working environment, but ASP.NET Core MVC provided more features to help with the development of the prototypes. Both frameworks had good documentation but the one for ASP.NET Core was deemed to be more thorough. As for the performance test the ASP.NET Core MVC prototype had faster response times overall, but with higher amounts of data and under higher load the CodeIgniter prototype performed better in some tests.

Today, digital development is happening at such a high rate that security is not as prioritized as it should be. When security is prioritized away, there is a high risk that vulnerabilities arise that malicious actors would like to exploit. It can be for accessing sensitive information, financial gain or simply bringing harm. In order for IT-security personnel to be able to more easily prevent and focus efforts on the vulnerabilities that are current today, this study aims to answer the following question: What is the trend of the most prevalent vulnerabilities? What is the trend of product owners with the most vulnerabilities? What is the trend based on the severity linked to the vulnerabilities? What is the trend of the impact on confidentiality, integrity, and accessibility? To answer the questions, a quantitative data analysis was done on the database from the National Vulnerability Database (NVD) together with the dataset from the Common Weakness Enumeration (CWE). The data set from CWE has been used to name and classify the vulnerabilities in NVD. Trends that have been identified in the analysis are as follows: injection, insufficient data authentication and uncontrolled resource consumption are vulnerabilities that have increased percentage every year since 2016. The impact of availability on the reported vulnerabilities declines as a percentage over the years. Vulnerabilities with a high impact on integrity, accessibility and confidentiality has decreased as a percentage.

Fault detection for residential photovoltaic power systems is an often-ignored problem. This thesis introduces a novel method for detecting power losses due to faults in solar panel performance. Five years of data from a residential system in Dalarna, Sweden, was applied on a random forest regression to estimate power production. Estimated power was compared to true power to assess the performance of the power generating systems. By identifying trends in the difference and estimated power production, faults can be identified. The model is sufficiently competent to identify consistent energy losses of 10% or greater of the expected power output, while requiring only minimal modifications to existing power generating systems.

The location-allocation problem has been studied over 50 years and recently a

new method called “gravity p-median model” is introduced to the public. The key

idea is that the probability of customers visit a facility is based on a distance decay

function rather than directly choose the nearest one. An empirical test shows that the

solution of gravity p-median model with exponential distance decay function is

unstable and of limit use.

This paper extends the research to apply gravity p-median model with three

different distance decay functions in Dalecarlia, Sweden. The distance decay

functions are estimated from a Swedish survey by maximum likelihood estimation.

The models are optimized by simulated annealing. The result suggests that different

distance decay functions dominate the solutions of gravity p-median model, and the

log-normal decay function can provide stable solutions.

The connection between breast and prostate cancer in relatives within a family is an intriguing question in the field since this information is valuable when diagnosing patients with either type of cancer as it may contribute to cancer prevention. The aim of the thesis is to ascertain whether these two cancers are inherited together. Markov Chain Monte Carlo estimation is used with the MCMCglmm package in R. The data used was the Minnesota Breast Cancer Study with up to five generations in families. The data consists of 28081 individuals in 426 families. Results show that the heritability for prostate cancer is 65% and 34% for breast cancer in the liability scale, regardless of other factors that may increase the risk of these cancers. The odds ratio of having breast cancer given the brother has prostate cancer is increased 1.59 times whilst the odds ratio of having prostate cancer given the sister has breast cancer is 1.58 times. This information can undoubtedly be useful to doctors to enable them to prevent the disease by bearing in mind the family history of both cancers.

It seems that a paper of mine appearing in Computational Statistics & Data Analysis (Carling, 2000) has prompted the development of outlier detection methods for highly skewed data. However, I wrote the paper in the spirit of Exploratory Data Analysis (Tukey, 1977) and I shared Tukey’s opinion, and I still hold it, that skewed data are better to be transformed for approximate symmetry prior to detection of outliers (or other data analyses).

In this chapter, I consider the cognitive biases arising in judgment under uncertainty that jeopardize good decision-making aligned with normative decision theories. This problem raises objections towards intuitive and fast decision-making. It would be appealing if training could be devised for reducing the biases, and I argue that such training is feasible. I relate best-practice in such training and advocate a number of topics to be included in such training for good, intuitive decision-making skills.

To finance transportation infrastructure and to address social and environmental negative externalities of road transports, several countries have recently introduced or consider a distance based tax on trucks. In competitive retail and transportation markets, such tax can be expected to lower the demand and thereby reduce CO₂ emissions of road transports. However, as we show in this paper, such tax might also slow down the transition towards e-tailing. Considering that previous research indicates that a consumer switching from brick-and-mortar shopping to e-tailing reduces her CO₂ emissions substantially, the direction and magnitude of the environmental net effect of the tax is unclear. In this paper, we assess the net effect in a Swedish regional retail market where the tax not yet is in place. We predict the net effect on CO₂ emissions to be positive, but off-set by about 50% because of a slower transition to e-tailing.

The synthetic control method (SCM) is a new, popular method developed for the purpose of estimating the effect of an intervention when only one single unit has been exposed. Other similar, unexposed units are combined into a synthetic control unit intended to mimic the evolution in the exposed unit, had it not been subject to exposure. As the inference relies on only a single observational unit, the statistical inferential issue is a challenge. In this paper, we examine the statistical properties of the estimator, study a number of features potentially yielding uncertainty in the estimator, discuss the rationale for statistical inference in relation to SCM, and provide a Web-app for researchers to aid in their decision of whether SCM is powerful for a specific case study. We conclude that SCM is powerful with a limited number of controls in the donor pool and a fairly short pre-intervention time period. This holds as long as the parameter of interest is a parametric specification of the intervention effect, and the duration of post-intervention period is reasonably long, and the fit of the synthetic control unit to the exposed unit in the pre-intervention period is good.

This paper aims to show how User Generated Content can create value for Smart Tourism Destinations. Applying the analysis on 5 different cases in the region of Stockholm to derive patterns and opportunities of value creation generated by UGC in tourism. Findings of this paper is also discussed in terms of improving decision making, possibilities of new business models and importance of technological improvements on STD’s. Finally, thoughts on models are presented for researchers and practitioners that might be interested in exploitation of UGC in the context of information-intensive industries and mainly in Tourism.

This study examines the effects of shadow banking on bank efficiency using data onChinese commercial banks during the period 1998–2012. I focus on two aspects: shadowbanking activities inside and outside the commercial banks. Stochastic frontier analysis(SFA) is used to analyze the effects of shadow banking on cost-efficiency. The empiricalresults indicate that the higher relative size of shadow banking inside the commercialbanks, the higher bank cost-efficiency is, while the higher relative size of shadow bankingoutside the commercial banks, the lower cost-efficiency is. This shows that there are gainsfrom shadow banking for the Chinese financial system. It is important for policymakers torealize this but at the same time understand that shadow banking likely implies a tradeoffbetween flexibility for the banking sector and higher risks.

This paper examines the electricity demand, and its determinants, in 29 European countries during the liberalization of the electricity market. Based on panel data for these countries for the years 1995–2015 and using a dynamic partial adjustment model, price elasticities are estimated for both residential and industrial electricity demand. These elasticities and effects of other variables on electricity consumption are estimated using both GMM (generalized method of moments) and ML (maximum likelihood) approaches. It is found that the price elasticities are very small, especially in the short run, while the income elasticities are relatively large, especially for households and in the long run.

Ett företags expansion kan finansieras med internt genererat kapital eller med hjälp av extern finansiering. Vi undersöker här hur omsättningstillväxten och överlevnadssannolikheten påverkas av företagets internt uppbyggda kapital. Undersökningen baseras på data över alla Sveriges aktiebolag under perioden 1997–2010.

Våra resultat indikerar att den initiala tillgången på internt uppbyggt kapital generellt sett inte är förknippad med en högre omsättningstillväxt eller ökade möjligheter att överleva på marknaden. Vi finner däremot att de företag som bygger upp interna medel innan de växer är mer sannolika att uppnå en långsiktigt hållbar tillväxt än de företag som växer innan de byggt upp ett internt kapital.

Resultaten indikerar också att företag som har varit med om uppköp eller sammanslagningar har både högre tillväxt och bättre chanser att överleva på marknaden.

Vi kan samtidigt konstatera att de skattade sambanden ofta är svaga trots det omfattande datamaterialet. Det tyder på att företagstillväxt i huvudsak är slumpmässig eller kan förklaras av variabler som är svåra att mäta och inkludera i kvantitativ analys. Detta bekräftas också av tidigare studier.

Våra resultat implicerar att framtida studier bör fokusera mer på hur företagen växer än på vad som kan förklara hur mycket de växer vid en viss tidpunkt.

Det antas ofta att det finns ett ”kapitalförsörjningsgap” i ekonomin på grund av asymmetrisk information, vilket leder till att efterfrågan på externt kapital är större än utbudet. De politiska beslutsfattarna vill därför ofta öka tillgången på externt riskkapital genom olika selektiva statliga stödinsatser för att få fler växande företag.

Klassiska teorier om företagstillväxt implicerar dock att det snarare är uppbyggnaden av företagets interna resurser som är av betydelse för att förklara företagens tillväxt och möjligheter att överleva på marknaden. Många företagare vill dessutom behålla kontrollen över sitt företag och kan därmed välja att inte växa med externt kapital, trots att de har tillväxtambitioner. Om detta stämmer är politiska beslut som ökar tillgången till internt kapital av större betydelse för företagens utveckling än politiska beslut som avser att öka tillgången på externt kapital.

I denna rapport vill vi studera hur betydelsefullt det internt uppbyggda kapitalet har varit för tillväxt och överlevnad hos aktiebolag i Sverige under perioden 1997–2010. Våra resultat indikerar att:

• Tillgången till internt kapital kan inte förklara den observerade företagstillväxten bland aktiebolag i Sverige under perioden 1997–2010.

• Det interna kapitalet är inte relaterat till överlevnadssannolikheten för aktiebolag i Sverige under perioden 1997–2010.

• Företag som växer efter att de har byggt upp internt kapital har större möjligheter att i framtiden kombinera hög tillväxt med hög lönsamhet jämfört med de företag som växer innan de har byggt upp internt kapital.

Resultaten är delvis motstridiga. De företag som bygger upp ett internt kapital och sedan väljer att expandera har en högre sannolikhet att nå en långsiktigt hållbar position för tillväxt än de företag som växer innan de har byggt upp internt kapital. Detta talar för att uppbyggnaden av internt kapital är av betydelse. Våra regressionsresultat indikerar dock att det inte finns något starkt positivt samband mellan internt uppbyggt kapital och företagens framtida omsättningstillväxt eller möjligheter att överlevna på marknaden.

Det finns ett antal möjliga förklaringar till våra resultat. Den första förklaringen är helt enkelt att det interna kapitalet inte är av betydelse för företagens framtida tillväxt och överlevnad. Den troliga förklaringen till detta är att vid jämvikt kommer företagarna att värdesätta en expansion med internt eller externt kapital på liknande sätt. Detta implicerar att det snarare är den totala mängden riskkapital som påverkar företagens tillväxt och överlevnad, och inte huruvida kapitalet genereras internt eller externt.

En annan tolkning är att företagarna fortfarande föredrar att växa med internt uppbyggt kapital eftersom resultaten visar effekten på företagstillväxten och överlevnadssannolikheten, givet den initiala uppbyggnaden av internt kapital i företagen. Den initiala nivån av internt kapital har således ingen effekt på företagsutvecklingen, men en ökning av det interna kapitalet i förhållande till tillgången på externt kapital kan fortfarande leda till att fler företag vill expandera sin verksamhet.

Många företagare i Sverige väljer också att inte växa trots att de har relativt god lönsamhetsutveckling. Detta kan betyda att vi inte observerar några samband mellan det internt uppbyggda kapitalet och företagens utveckling eftersom många företagare inte vill växa under de rådande institutionella förutsättningarna. Det finns med andra ord utelämnande variabler, till exempel olika tillväxtbarriärer (regelkrångel, strikt anställningsskydd, matchningsproblem, etc.), som kan förklara varför det interna kapitalet inte har någon observerad betydelse för tillväxten och företagens överlevnad i vår studie.

Slutligen kan en möjlig förklaring vara att företagens tillväxt och överlevnad i mycket hög utsträckning är slumpmässig. Detta innebär att det blir svårt att förklara vad det är som påverkar företagens utveckling, vilket också implicerar att selektiva stödåtgärder för att få fler växande företag är dömda att misslyckas.

Sammanfattningsvis kan vi konstatera att våra resultat inte ger något stöd för hypotesen att företag med tillgång till internt kapital växer snabbare och har en högre överlevnadssannolikhet än de företag som har tillgång till mindre internt uppbyggt kapital. Resultaten bygger på de rådande institutionella förutsättningarna i Sverige under perioden 1997–2010 och är avgränsade till att studera effekten av det initialt uppbyggda kapitalet i bolagen. Vi undersöker inte företagarnas preferenser, det vill säga om de föredrar mer internt kapital framför externt kapital när de ska expandera verksamheten, eller om en ökning av det internt uppbyggda kapitalet har en större tillväxteffekt än mer externt kapital.

The effects of a new IKEA store on retail revenues, employment and inflow of purchasing power in the entry municipalities as well as in neighbouring municipalities were investigated using data from 2000–11. A propensity score-matching method was used to find non-IKEA entry municipalities that were as similar as possible to the entry municipalities based on the situation before entry. The results indicate that IKEA entry increased entry municipality durable goods revenues by about 20% and employment by about 17%. Only small and, in most cases, statistically insignificant effects were found in neighbouring municipalities.

This chapter shows how elasticity testing of neural networks can greatly aid our understanding of transport systems. It examines several different pieces of work which have the common theme of using neural networks, coupled with a technique of elasticity analysis, in order to reach a better understanding of transport related problems. One of the main reasons for using neural networks is that they can easily represent complex functions, often with nonlinear interactions between different parameters. The chapter focuses on the elasticity of a single parameter with respect to a single network output. However, the elasticity technique can easily be extended to explore mutual interactions between parameters. A three-dimensional elasticity plot is shown of elasticity response against occupancy and speed. When neural networks are coupled with advanced computer visualization tools they provide an immensely powerful tool for general analysis. © V. Himanen, P. Nijkamp, A. Reggiani and J. Raitio 1998. All rights reserved.

Forgetting is an oft-forgotten art. Many artificial intelligence (AI) systems deliver good performance when first implemented; however, as the contextual environment changes, they become out of date and their performance degrades. Learning new knowledge is part of the solution, but forgetting outdated facts and information is a vital part of the process of renewal. However, forgetting proves to be a surprisingly difficult concept to either understand or implement. Much of AI is based on analogies with natural systems, and although all of us have plenty of experiences with having forgotten something, as yet we have only an incomplete picture of how this process occurs in the brain. A recent judgment by the European Court concerns the "right to be forgotten" by web index services such as Google. This has made debate and research into the concept of forgetting very urgent. Given the rapid growth in requests for pages to be forgotten, it is clear that the process will have to be automated and that intelligent systems of forgetting are required in order to meet this challenge.

The performance of supervised machine learning algorithms is highly dependent on the distribution of the target variable. Infrequent values are more di_cult to predict, as there are fewer examples for the algorithm to learn patterns that contain those values. These infrequent values are a common problem with real data, being the object of interest in many _elds such as medical research, _nance and economics, just to mention a few. Problems regarding classi_cation have been comprehensively studied. For regression, on the other hand, few contributions are available. In this work, two ensemble methods from classi_cation are adapted to the regression case. Additionally, existing oversampling techniques, namely SmoteR, are tested. Therefore, the aim of this research is to examine the inuence of oversampling and ensemble techniques over the accuracy of regression models when predicting infrequent values. To assess the performance of the proposed techniques, two data sets are used: one concerning house prices, while the other regards patients with Parkinson's Disease. The _ndings corroborate the usefulness of the techniques for reducing the prediction error of infrequent observations. In the best case, the proposed Random Distribution Sample Ensemble reduced the overall RMSE by 8.09% and the RMSE for infrequent values by 6.44% when compared with the best performing benchmark for the housing data set.

A risk is something that can happen and a problem is something that we know will happen or that has already happened. Security testing is used to evaluate a programs security using various methods and risk-based security testing is used to analyze, calculate and correct potential defects or problems in a system.Testing can be very costly and it is the most primary way of removing software defects. Many people focus their testing looking for correct behavior and not deviant behaviors in software, therefore security testing has not been as relevant in traditional testing. It is usually not possible to perform exhaustive testing on a system, instead you must selectively choose tests to conduct. How should the selection of tests be conducted? The study therefore intends to investigate how one can start working with risk-based security testing in development projects in order to prioritize and choose test cases and test methods. The study also aims to answer whether you can get any financial or practical benefits from working with risk-based security testing. To conduct the study a case study was used and to collect data, a document study was used to provide the opportunity to answer the questions. In order to analyze the collected data, a qualitative data analysis method has been used to explain and describe the content with a descriptive research approach. The results of the study provided an example of risk management with different steps one can take to start working on risk-based security testing in existing or new development projects. The study’s conclusion also shows that if you work with risk-based security testing there are practical benefits. For instance, higher quality of the system and economic benefits by finding defects or implementing countermeasures for possible risks at an early stage during the systems development.

This article explores whether GTS (gaze time on screen) can be useful as an engagement measure in the screen mediated learning context. Research that exemplifies ways of measuring engagement in the on-line education context usually does not address engagement metrics and engagement evaluation methods that are unique to the diverse contemporary instructional media landscape. Nevertheless, unambiguous construct definitions of engagement and standardized engagement evaluation methods are needed to leverage instructional media's efficacy. By analyzing the results from a mixed methods eye-tracking study of fifty-seven participants evaluating their visual and assembly performance levels in relation to three visual, procedural instructions that are versions of the same procedural instruction, we found that the mean GTS-values in each group were rather similar. However, the original GTS-values outputted from the ET-computer were not entirely correct and needed to be manually checked and cross validated. Thus, GTS appears not to be a reliable, universally applicable automatic engagement measure in screen-based instructional efforts. Still, we could establish that the overall performance of learners was somewhat negatively impacted by lower than mean GTS-scores, when checking the performance levels of the entire group (N = 57). When checking the stimuli groups individually (N = 17, 20, 20), the structural diagram group's assembly time durations were positively influenced by higher than mean GTS-scores.

Purpose: To explore consumers’ attitudes towards e-commerce, in particular online grocery shopping, and its delivery in non-dense areas for the purpose of designing smart last-mile solutions.

Approach: The state-of-the-art of smart e-commerce delivery in dense areas was identified by a review of the literature. It was expected that this knowledge could be transferred to non-dense areas. This prediction was examined and explored further by making use of four focus groups recruited in a Swedish mid-sized town.

Findings: Respondents were generally positive towards e-commerce, although mixed attitudes were found with regard to online grocery shopping. Further, the willingness to pay for flexible, smart and sustainable delivery was low, with a notable exception for local produce.

Originality: The knowledge acquired and solution developed in dense areas is not readily transferred to non-dense areas. There is scope for developing new Business Models for the supply chain of local produce. For the prototype testing and roll-out of smart e-commerce delivery platforms, the online local produce market is recommended.

This study explores mathematics teachers’ conceptions of how the physical environment in classrooms affects their students’ chances for learning. Semi structured interviews were performed with a few Swedish teachers with experience from tackling different physical settings when teaching mathematics. When analysing the interview transcripts preliminary findings are that: teachers appreciate flexibility and control over the physical settings in the classroom; inadequate acoustics are extra problematic in mathematical activities involving verbal interactions between students in small groups; mathematics task solving in peace and quiet is a common part of mathematics lessons and it easily gets disturbed by external noise.

The problem addressed in this thesis is that a considerable proportion of students around the world attend school in inadequate facilities, which is detrimental for the students’ learning outcome. The overall objective in this thesis is to develop a methodology, with a novel approach to involve teachers, to generate a valuable basis for decisions regarding design and improvement of physical school environment, based on the expressed needs for a specific school, municipality, or district as well as evidence from existing research. Three studies have been conducted to fulfil the objective: (1) a systematic literature review and development of a theoretical model for analysing the role of the physical environment in schools; (2) semi structured interviews with teachers to get their conceptions of the physical school environment; (3) a stated preference study with experimental design as an online survey. Wordings from the transcripts from the interview study were used when designing the survey form. The aim of the stated preference study was to examine the usability of the method when applied in this new context of physical school environment. The result is the methodology with a mixed method chain where the first step involves a broad investigation of the specific circumstances and conceptions for the specific school, municipality, or district. The second step is to use the developed theoretical model and results from the literature study to analyse the results from the first step and transform them in to a format that fits the design of a stated preference study. The final step is a refined version of the procedure of the performed stated preference study.

In this paper, we develop an analytical tool for the role of the physical environment in mathematics education. We do this by extending the didactical triangle with the physical environment as a fourth actor and test it in a review of literature concerning the physical environment and mathematics education. We find that one role played by the physical environment, in relation to mathematical content, is to portray the content in focus, such as geometry and scale. When focusing on teachers, students, and the interaction between them, the role of the physical environment appears to be a precondition, either positive (enabling) or negative (hindering). Many of the findings are valid for education in general as well, such as the importance of building status.

Many individuals and businesses make decisions based on freely and easily accessible online reviews. This provides incentives for the dissemination of fake reviews, which aim to deceive the reader into having undeserved positive or negative opinions about an establishment or service. With that in mind, this work proposes machine learning applications to detect fake online reviews from hotel, restaurant and doctor domains. In order to _lter these deceptive reviews, Neural Networks and Support Vector Ma- chines are used. Both algorithms' parameters are optimized during training. Parameters that result in the highest accuracy for each data and feature set combination are selected for testing. As input features for both machine learning applications, unigrams, bigrams and the combination of both are used. The advantage of the proposed approach is that the models are simple yet yield results comparable with those found in the literature using more complex models. The highest accuracy achieved was with Support Vector Machine using the Laplacian kernel which obtained an accuracy of 82.92% for hotel, 80.83% for restaurant and 73.33% for doctor reviews.

Many studies have been done on the identification of the causal effect of a treatment (or intervention) but still the field of causal effect identification is highly debated in many disciplines e.g. data science, econometrics, biostatistics etc. This study examined the theoretical motivation of predictive modelling approach to estimate and check the sensitivity analysis for causal effects by using structural equations modelling framework with application of real data from Landstinget Dalarna. In this study, the link between causal inference using structural equations modelling approach of Heckman's two-step estimation framework and predictive modeling approach has been established. Furthermore, two different simulation studies under linearity and nonlinearity assumptions were conducted to see the finite sample properties of the predictive modelling approaches such as Support Vector Machine, Random Forest, Gaussian boosted regression trees and Bayesian additive regression trees. Finally, the predictive modelling approaches were used to estimate the treatment effects on patient outcomes in terms of length of stay, unplanned readmission and mortality within 30 days after discharge. The results were also compared with the traditional approaches: propensity score matching, propensity weighting and two-step estimation method. This study used two types of estimates, average treatment effects and treatment on treated. The estimates and their standard errors were calculated for the real data from Landstinget Dalarna. The study found that, non-outlying patients are staying in the hospital for longer periods of time (in days) compared to outlying patients though the reasons that make long of stay remained unknown. For readmission and mortality, the results varied a lot between the alternative models, and we can therefore not conclude that non-outlying patients have higher mortality and readmission rates compared to outlying.

Due to growing population over the past decades, settlements are scattered in sparse as well as dense clusters across Dalarna County. However, irrespective of any physical, social or economic conditions, free public health care must be available at a minimum and equal distance of travel for all citizens of a region. In the current scenario in Dalarna, around 16% of the population travels beyond 10 km to reach their nearest medical facility. The aim of this study is to suggest the most favorable locations for Mobile Hospital services across Dalarna County, based on spatial analysis of accessibility, population coverage, and importantly, in a way that travel distance, is minimized and equal for all. This study makes use of Multi Criteria Analysis methods. The problem of mobile hospital site selection is broken down into criteria, and Analytic Hierarchical Process is used to evaluate weights for each criterion. Then, a weighted overlay results in regions with score-based suitability for a mobile hospital. Maximum population coverage based Location Allocation analysis results in generating a proposed Facility and Demand Coverage output. The results show an increase in coverage of population, while meeting the requirements of criteria in the aim.

The overall aim of this study is to understand how average daily individuals’ accessibility to other individuals has changed in Sweden and what the impact of changes in intra-travel time is on changes in daily individuals’ accessibility in Dalarna County.

This thesis was conducted by applying quantitative research method via secondary data collection method. The required data for the purpose of this study were collected from Official Statistic of Sweden (SCB), Swedish Road Administration (NVDB) and Swedish National Travel Survey (RVU). Research population or target population for this study is all Swedish workforce population, aged 20-64. For the first part of the aim, the entire research population has been investigated and for the second part of the aim, non-probability sampling method (purposive sampling method) has been applied. The datasets have been applied to compute different variables. The variables were computed by using formulas extracted from previous empirical studies and with help of GIS and R software. The relationship between response and predictors variables has been statistically analyzed by multiple linear regression.

The findings indicate that average daily individuals’ accessibility increased within the Swedish context between the years 1990 and 2008. It was found that the most increment was related to years 1995 to 2000. Also the statistical analysis showed that the relationship between the changes in average intra-travel time and changes in average daily individuals’ accessibility was not significant in municipalities in Dalarna County. Meanwhile, it was concluded that among predictor variables, changes in average daily mobility had a significant relationship with the changes in average daily individuals’ accessibility to other individuals within municipalities in Dalarna County.