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  • 51.
    Jia, Siqi
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    How fair is the so called fair method for resetting the targetin the interrupted men’s one-day international cricket matches?2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Duckworth-Lewis (DL) method is used in the One Day International (ODI) cricketmatches when the matches are interrupted. However, all information we have about thismethod is the Duckworth-Lewis calculator, which leads us to suspect the fairness of it.This thesis quantified the effect of the DL method on the result of the matches, whichmeans if teams have the same winning chance under DL method or not. The effect oflikely influential factors, including the use of DL method, on the winning odds areestimated by using the Generalized Linear Mixed Model. The results indicates that theDL method does not have any significant effect on the winning odds, nor does the DLmethod change the effects of other factors. The results also confirm that homeadvantage exists and that winning the coin toss does not affect the outcome of match.

  • 52. Johansson, D.
    et al.
    Ericsson, A.
    Johansson, A.
    Medvedev, A.
    Nyholm, D.
    Ohlsson, F.
    Senek, M.
    Spira, J.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Individualization of levodopa treatment using a microtablet dispenser and ambulatory accelerometry2018In: CNS Neuroscience & Therapeutics, ISSN 1755-5930, E-ISSN 1755-5949, Vol. 24, no 5, p. 439-447Article in journal (Refereed)
    Abstract [en]

    Aim

    This 4‐week open‐label observational study describes the effect of introducing a microtablet dose dispenser and adjusting doses based on objective free‐living motor symptom monitoring in individuals with Parkinson's disease (PD).

    Methods

    Twenty‐eight outpatients with PD on stable levodopa treatment with dose intervals of ≤4 hour had their daytime doses of levodopa replaced with levodopa/carbidopa microtablets, 5/1.25 mg (LC‐5) delivered from a dose dispenser device with programmable reminders. After 2 weeks, doses were adjusted based on ambulatory accelerometry and clinical monitoring.

    Results

    Twenty‐four participants completed the study per protocol. The daily levodopa dose was increased by 15% (112 mg, < 0.001) from period 1 to 2, and the dose interval was reduced by 12% (22 minutes, P = 0.003). The treatment adherence to LC‐5 was high in both periods. The MDS‐UPDRS parts II and III, disease‐specific quality of life (PDQ‐8), wearing‐off symptoms (WOQ‐19), and nonmotor symptoms (NMS Quest) improved after dose titration, but the generic quality‐of‐life measure EQ‐5D‐5L did not. Blinded expert evaluation of accelerometry results demonstrated improvement in 60% of subjects and worsening in 25%.

    Conclusions

    The introduction of a levodopa microtablet dispenser and accelerometry aided dose adjustments improve PD symptoms and quality of life in the short term.

  • 53.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Automatic trigger speed for vehicle activated signs using Adaptive Neuro fuzzy system and Random ForestIn: International Journal on Advances in Intelligent Systems, ISSN 1942-2679, E-ISSN 1942-2679Article in journal (Refereed)
  • 54.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Predicting automatic trigger speed for vehicle-activated signs2018In: Journal of Intelligent Systems, ISSN 0334-1860, E-ISSN 2191-026XArticle in journal (Refereed)
    Abstract [en]

    Vehicle-activated signs (VAS) are speed-warning signs activated by radar when the driver speed exceeds a pre-set threshold, i.e. the trigger speed. The trigger speed is often set relative to the speed limit and is displayed for all types of vehicles. It is our opinion that having a static setting for the trigger speed may be inappropriate, given that traffic and road conditions are dynamic in nature. Further, different vehicle classes (mainly cars and trucks) behave differently, so a uniform trigger speed of such signs may be inappropriate to warn different types of vehicles. The current study aims to investigate an automatic VAS, i.e. one that could warn vehicle users with an appropriate trigger speed by taking into account vehicle types and road conditions. We therefore investigated different vehicle classes, their speeds, and the time of day to be able to conclude whether different trigger speeds of VAS are essential or not. The current study is entirely data driven; data are initially presented to a self-organising map (SOM) to be able to partition the data into different clusters, i.e. vehicle classes. Speed, time of day, and length of vehicle were supplied as inputs to the SOM. Further, the 85th percentile speed for the next hour is predicted using appropriate prediction models. Adaptive neuro-fuzzy inference systems and random forest (RF) were chosen for speed prediction; the mean speed, traffic flow, and standard deviation of vehicle speeds were supplied as inputs for the prediction models. The results achieved in this work show that RF is a reliable model in terms of accuracy and efficiency, and can be used in finding appropriate trigger speeds for an automatic VAS. 

  • 55.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A comparative study between vehicle activated signs and speed indicator devices2017In: Transportation Research Procedia, ISSN 2324-9935, E-ISSN 2352-1465, Vol. 22, p. 115-123Article in journal (Refereed)
    Abstract [en]

    Vehicle activated signs and Speed indicator devices are safety signs used to warn and remind drivers that they are exceeding the speed limit on a particular road segment. This article has analysed and compared such signs with the aim of reporting the most suitable sign for relevant situations. Vehicle speeds were recorded at different test sites and the effects of the signs were studied by analyzing the mean and standard deviation. Preliminary results from the work indicate that both types of signs have variable effects on the mean and standard deviation of speed on a given road segment. Speed indicator devices were relatively more effective than vehicle activated signs on local roads; in contrast their effectivity was only comparable when tested on highways.

  • 56.
    Kogo, Gloria
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Analyzing automatic cow recordings to detect the presence of outliers in feed intake data recorded from dairy cows in Lovsta farm2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Outliers are a major concern in data quality as it limits the reliability of any data. The

    objective of our investigation was to examine the presence and cause of outliers in the system

    for controlling and recording the feed intake of dairy cows in Lovsta farm, Uppsala Sweden.

    The analyses were made on data recorded as a timestamp of each visit of the cows to

    the feeding troughs from the period of August 2015 to January 2016. A three step

    methodology was applied to this data. The first step was fitting a mixed model to the

    data then the resulting residuals was used in the second step to fit a model based

    clustering for Gaussian mixture distribution which resulted in clusters of which 2.5% of

    the observations were in the outlier cluster. Finally, as the third step, a logistic

    regression was then fit modelling the presence of outliers versus the non-outlier

    clusters. It appeared that on early hours of the morning between 6am to 11.59am, there

    is a high possibility of recorded values to be outliers with odds ratio of 1.1227 and this

    is also the same time frame noted to have the least activity in feed consumption of the

    cows with a decrease of 0.027 kilograms as compared to the other timeframes. These

    findings provide a basis for further investigation to more specifically narrow down the

    causes of the outliers.

  • 57.
    Laryea, Rueben
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A data-driven decision support system for coherency of experts’ judgment in complex classification problems: The case of food security as a UN sustainable development goal2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Everyday humans need to make individual or collective decisions. Often the decisions aim at achieving multiple goals (thus involving multiple criteria) and rely on the decision maker(s)’ intuition, internal data, as well as external sources of data. Faced with a complex decision problem of this kind, it is a great challenge to decisionmakers to be logically coherent over time with regard to their preferences. To aid in achieving coherency, operation researchers and decision analysts have developed formal methods to support decision makers. One such method is the UTADIS method that serves as the workhorse for this thesis. I received the request from UN officials who had to manage the sustainable development goals while addressing the issue of food security. They wished for a decision support system (DSS) that could aid in their classification of countries to mitigate the risk of failing on food security. The virtue of the DSS should be that their expert judgment was complemented by formal methods for better risk classification. The UTADIS method was fitting for the purpose, but it lacked implementability. In particular, it required an iterative approach engaging the experts multiple times, while not readily lending itself to making use of external data, making it inefficient as a DSS. The fundamental contribution of this thesis is that I have solved these shortcomings of the UTADIS method, such that it now readily can be used in a functionally efficient way for the desired purpose of the UN. In solving these problems, it is also more broadly implementable as a DSS, as I have validated the artifact to a DSS, by use of several demonstrations and exposed it to sensitivity analysis.

  • 58.
    Laryea, Rueben
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Cialani, Catia
    Dalarna University, School of Technology and Business Studies, Economics.
    A Food Price Volatility Model for Country Risk Classification2018In: International Journal of Risk Assessment and Management, ISSN 1466-8297, E-ISSN 1741-5241Article in journal (Refereed)
    Abstract [en]

    Decision makers require risk models which satisfies their preferences in decision making processes. A methodological approach to presenting a decision model that satisfies the preferences of the decision maker and aids the decision maker to classify countries into crisis groups based on the price volatility of food staple criteria is discussed in this paper. The price volatility of food staples is obtained from time series plots and a Multi-Criteria Decision Analysis method, the UTilitdditives DIScriminantes (UTADIS) classification methodological framework is applied on the price volatility data to develop a food price volatility classification model which suits the decision maker’s preferences. The methodological framework is better applied in this paper by aiding the decision maker to make informed judgements on the price volatility of food staples in predefining their risk classes. This introduces efficiency in the application of the methodological classification framework, by reducing to the barest minimum level, the misclassification errors between the decision makers preferred classification and the UTADIS method’s classification which estimates the utility function or classification model and the utility threshold or cut-off points which would classify the country alternatives into their authentic or original classes with the execution of the methodological framework just once. The resulting utility function or classification model is thus accurate enough to satisfy the preferences of the decision maker in classifying future datasets.

  • 59.
    Laryea, Rueben
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Cialani, Catia
    Dalarna University, School of Technology and Business Studies, Economics.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Sensitivity analysis of a risk classification model for food price volatility2018In: International Journal of Risk Assessment and Management, ISSN 1466-8297, E-ISSN 1741-5241, Vol. 21, no 4, p. 374-382Article in journal (Refereed)
    Abstract [en]

    A sensitivity analysis to vary the weights of an accurate predictive classification model to produce a mixed model for ranking countries on the risk of food price volatility is carried out in this paper. The classification model is a marginal utility function consisting of multiple criteria. The aim of the sensitivity analysis is to derive a mixed model to be used in ranking of country alternatives to aid in policy formulation. Since in real-life situations the data that goes into decision making could be subjected to possibilities of alterations over time, it is essential to aid decision makers to vary the weights of the criteria using both subjective and objective information to introduce imprecision and to generate relative values of the criteria with a scale to form a mixed model. The mixed model can be used to rank future relative alternative value data sets for policy formulation.

  • 60.
    Laryea, Rueben
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Farsari, Ioanna
    Dalarna University, School of Technology and Business Studies, Tourism Studies.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    A Decision Tool Approach to Sensitivity Analysis in a Risk Classification Model2018In: Article in journal (Refereed)
    Abstract [en]

    A Decision Analytical tool capable of handling numerically imprecise data for decision making is used in this paper to analyze the risk of the effect of data alteration in the ranking positions of country alternatives for food price volatility. Unguided decision making processes would lead to non-optimal decisions with it’s dire consequences on the resources of organizations. The paper is thus guided by the use of an accurate risk classification model to implement uncertainty and imprecision which are essential part of real life decision making processes with computer based tools to overcome the problem of possibilities uncertain and imprecise input data of criteria and alternatives. A ranking of the alternatives is conducted after imprecision is considered in the input data and a further analysis is carried out to determine which criteria is sensitive enough to alter the position of a country in the rankings.

  • 61.
    Li, Boyan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A comparison of hurdle method and universal kriging for predicting spatially correlated count response with excessive zeros2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    A hurdle model combined with Bernoulli part and truncated Poisson part can be used to predict zero-inflated geographic count response. To get the prediction with a hurdle model, the estimation of fixed effects can be easily solved as generalized linear model (GLM) does. An ad-hoc method, which re-fits the hurdle model to compute the predicted random effect for geographic IDs with missing response, is applied. However, no study has examined the performance of this prediction method for hurdle model, especially for the spatially correlated count responses with excessive zeros. This paper aims to check how well the hurdle predictors perform in ideal and real situations, by means of cross validation. The performance of the hurdle model based prediction is compared with the performance of the predictors from the universal kriging which is most widely used on spatial predictions. The simulation result shows that hurdle performs better than universal kriging based on mean absolute errors. The ideal situation is generated by using Monte-Carlo simulation. In order to examine the comparative performance with real data situations, two real data examples are presented. The results show that, in prediction using single observation per location (e.g. one year’s spatial observation) with excessive zeros, hurdle model does not perform well, while universal kriging also failed in the same situations especially for those non-zero points.

  • 62.
    Li, Yujiao
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Who benefits when IKEA enters local markets in Sweden?: An empirical assessment using difference-in-difference analysis, synthetic control methods, and Twitter sentiment analysis2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Policy makers often spend considerable amounts of money to attract IKEA to their region despite not having any empirical measurements on its expected contribution to the local economy. As such, an empirical study of the economic and social impact of new IKEA stores can aid political decision making, and contribute to the literature regarding how big-box retail entry affects the regions where they enter.

    This dissertation aims to estimate: the impact of IKEA entry on incumbent retailers productivity, and investigate if the impact is heterogenus depending on local maket size, type of retail industry, distance to surrounding retailers, and firm size; IKEA entry effects on the average labor productivity in durable goods retailing in the entry regions; and, finally, public opinions regarding  IKEA entry.

    For IKEA entry effects on incumbent retailers, Paper I~III separately examine four factors of potential heterogeneity. Paper I finds that market size matters: smaller rural regions have bigger IKEA effects. Paper II considers two factors: firm industry and distance, and confirms that IKEA entry effects dissipate over distance. The positive impact of IKEA entry on incumbent retailers is limited to those selling complementary goods to IKEA. No positive effects were found for the urban entry in Gothenburg in the two first papers, which is somewhat surprising. Paper III found that a positive effect exist also in Gothenburg, but it is limited to relatively small incumbent retailers with a capital stock below 1 500 000 SEK. Policy making tends to consider IKEA overall effects on entry municipalities besides IKEA spillover effects on firms. Paper V shows that rural regions are affected by IKEA entry, while larger urban markets are not.

    For the social effects of IKEA, Paper VI uses Twitter text mining to study public opinions regarding IKEA entry into local markets. The new IKEA stores under study caught significant public attention at the time of entry, with mostly positive attitudes toward the new stores. The favorite topics for discussion at the time of the different IKEA entries were heterogeneous depending on location.

    Methodologically, Paper I uses traditional Difference-in-Difference (DID) to have an initial understanding of IKEA entry spillover effects in four regions; Paper II extends to Spatial DID to catch the spatial interaction between firms; Paper III uses Panel Smooth Transition Regression to identify heterogenous effects due to firms size. Paper IV investigates a new treatment effects estimation aproach, Synthetic Control Method (SCM), to explore when the SCM is powerful, and how to improve its performance; Paper V then uses SCM to estimate IKEA effects at municipality level. In addition, to make SCM developed readily available for other researchers, the author of this thesis also published one web-application to implement a synthetic control method power test, and another to implement parametric & non-parametric estimation and inference.  

    These findings confirm that IKEA has a positive effect on the regions where they enter. Nevertheless, governments that are to decide if to allow a big-box retail entry into their local community should be aware that the impact of such entry will depend on the size of the existing retail market, the type of existing retail industry, and the size of existing retailers in the entry region.

  • 63.
    Li, Yujiao
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Twitter Sentiment Analysis of New IKEA Stores Using Machine Learning2018In: 2018 International Conference on Computer and Applications, ICCA 2018, 2018, p. 4-11, article id 8460277Conference paper (Refereed)
    Abstract [en]

    This paper studied public emotion and opinion concerning the opening of new IKEA stores, specifically, how much attention are attracted, how much positive and negative emotion are aroused, what IKEA-related topics are talked due to this event. Emotion is difficult to measure in retail due to data availability and limited quantitative tools. Twitter texts, written by the public to express their opinion concerning this event, are used as a suitable data source to implement sentiment analysis. Around IKEA opening days, local people post IKEA related tweets to express their emotion and opinions on that. Such “IKEA” contained tweets are collected for opinion mining in this work. To compute sentiment polarity of tweets, lexiconbased approach is used for English tweets, and machine learning methods for Swedish tweets. The conclusion is new IKEA store are paid much attention indicated by significant increasing tweets frequency, most of them are positive emotions, and four studied cities have different topics and interests related IKEA. This paper extends knowledge of consumption emotion studies of prepurchase, provide empirical analysis of IKEA entry effect on emotion. Moreover, it develops a Swedish sentiment prediction model, elastic net method, to compute Swedish tweets’ sentiment polarity which has been rarely conducted.  

  • 64.
    Li, Yujiao
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Human Geography.
    Mihaescu, Oana
    HUI Research, Stockholm, Sweden.
    Rudholm, Niklas
    HUI Research, Stockholm, Sweden.
    Agglomeration economies in urban retailing: Are there productivity spillovers when big-box retailers enter urban markets?Manuscript (preprint) (Other academic)
    Abstract [en]

    Previous studies have found that big-box retail entry does not affect the productivity of incumbent retailers when entry occurs in urban areas. In this paper, we show that there are positive spillover effects of big-box retail entry to incumbent retailers in urban areas as well, but that these are limited to relatively small retailers, making the effects difficult to detect using traditional econometric methods, such as difference-in-difference estimation on the full sample of firms. In a two-step procedure, we first use panel smooth transition regression to determine size thresholds that delimit incumbent retail firms by their possible reactions to the new big-box entry. We then use difference-in-difference estimations on these subgroups of firms to determine, within each group, the direction and magnitude of the effects of big-box entry on their productivity. For the group of small incumbent retailers, we find positive spillover effects on productivity of approximately 9%.

  • 65.
    Lin, Chenlu
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A Combined Approach to Recommendation Systems: A case study of data analysis for hotel ratings2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Recommendation systems are used to improve the convenience and efficiency for users tobook hotels. The most widely used method in recommendation systems is collaborativefiltering. A critical step of the collaborative filtering method is to analyze one user'spreference and recommend products or services to the user based on other similar users'preferences. However, collaborative filtering is vulnerable for recommendation when it isdifficult to obtain user preferences, in the situation where e.g. a user provides none or veryfew comments on products or services. The problem occurring in this situation is called thecold start problem. This thesis proposes an improved method which combines collaborativefiltering with data classification to recommend suitable hotels to new users. The accuracy ofthe recommendation is determined by the rankings so that evaluations are conducted on theTop-3 and the Top-10 recommendation lists using the 10-fold cross-validation method andROC curves. The results show that the Top-3 hotel recommendation list proposed by thecombined method has the superiority of the recommendation performance than the Top-10 listunder the cold start condition in most of the times.

  • 66.
    Luo, Xin
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    The causal effect of fertility on Swedish mothers’ labor supply2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The objective of this thesis is to estimate causal effect of childbearing on women’slabor supply in Sweden. I follow the approach suggested by Angrist and Evans (1998)using parental preferences for a mixed child-gender composition as an exogenoussource of variation in women’s fertility. The results show that having an additionchild have a negative effect on women’s working hours. However, none of theseeffects are statistical significant and the value of F-statistic and partial-R2 are rathersmall, all suggest that the same-sex is very likely a weak instrument in Sweden.

  • 67.
    Macuchova, Zuzana
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Brandt, Daniel
    Dalarna University, School of Technology and Business Studies, Human Geography.
    Vinterturismens utveckling 2012-2017: En kartläggning av gästnätternas fördelning och utveckling på kommunnivå i Dalarnas län2017Report (Other academic)
  • 68.
    Mahbub, Cynthia
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    The match of demand and supply of public transportation (bus) services in Borlänge, Dalarna.2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Accessibility to public transport service allows mobility of people who do not have access to private cars and at the same time reduces adverse effects of motorized vehicles such as energy consumption, air pollution, etc. Government body promotes to use public transport to facilitate better living condition. However, a critical issue remains whether the public transportation services are sufficient to meet the demanded public transportation services.

    In this research, particular attention has been paid to the spatial transport service gap assessment by analyzing the demand and the supply of the public transportation services in Borlänge. The spatial aspects have been chosen based on Swedish socio-economic condition. The aim of the research is to find a generic methodology to ascertain the disparity between public transport demand and available supply of public transport especially on bus line 211, 213 & 216 in Borlänge Municipality and to visualize the disparity of transportation service using Geographical Information System (GIS) application at different areas along the bus line.

    The result indicates that existing public transport provided by Dalatrafik has a significant gap in Tronsjö, Milsbosjön, Milsbo and Viksnäs between delivered transport supply and possible transportation service needed. This transportation gap may occur due to the deficiency of service capacity and low frequency of the services. Moreover, some topics can be explored for further research such as temporal service gap analysis at each area, find alternative means of transport, flexible transportation service and etc. to improve the public transportation system in Borlänge.

  • 69.
    Malek, Wasim
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Big Data Analysis in Social Networks: Extracting Food Preferences of Vegans from Twitter2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Market research is often conducted through conventional methods such as surveys, focus

    groups and interviews. But the drawbacks of these methods are that they can be costly and timeconsuming.

    This study develops a new method, based on a combination of standard techniques

    like sentiment analysis and normalisation, to conduct market research in a manner that is free

    and quick. The method can be used in many application-areas, but this study focuses mainly on

    the veganism market to identify vegan food preferences in the form of a profile.

    Several food words are identified, along with their distribution between positive and negative

    sentiments in the profile. Surprisingly, non-vegan foods such as cheese, cake, milk, pizza and

    chicken dominate the profile, indicating that there is a significant market for vegan-suitable

    alternatives for such foods. Meanwhile, vegan-suitable foods such as coconut, potato,

    blueberries, kale and tofu also make strong appearances in the profile.

    Validation is performed by using the method on Volkswagen vehicle data to identify positive

    and negative sentiment across five car models. Some results were found to be consistent with

    sales figures and expert reviews, while others were inconsistent. The reliability of the method

    is therefore questionable, so the results should be used with caution.

  • 70.
    Martín-Roldán Villanueva, Gonzalo
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Household’s energy consumption and productionforecasting: A Multi-step ahead forecast strategiescomparison.2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In a changing global energy market where the decarbonization of the economy and

    the demand growth are pushing to look for new models away from the existing

    centralized non-renewable based grid. To do so, households have to take a

    ‘prosumer’ role; to help them take optimal actions is needed a multi-step ahead

    forecast of their expected energy production and consumption. In multi-step ahead

    forecasting there are different strategies to perform the forecast. The single-output:

    Recursive, Direct, DirRec, and the multi-output: MIMO and DIRMO. This thesis

    performs a comparison between the performance of the differents strategies in a

    ‘prosumer’ household; using Artificial Neural Networks, Random Forest and

    K-Nearest Neighbours Regression to forecast both solar energy production and

    grid input. The results of this thesis indicates that the methodology proposed

    performs better than state of the art models in a more detailed household energy

    consumption dataset. They also indicate that the strategy and model of choice is

    problem dependent and a strategy selection step should be added to the forecasting

    methodology. Additionally, the performance of the Recursive strategy is always

    far from the best while the DIRMO strategy performs similarly. This makes the

    latter a suitable option for exploratory analysis.

  • 71.
    May, Ross
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    An Empirical Investigation of the Merits of a Classof Analytically Tractable Matern CovarianceStructures in Spatial Data Analysis2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    I investigate, using the R package

    spaMM, the effect of misspecification of the

    smoothing parameter,

    􀁑, of the Matern covariance structure on the mean part of

    hierarchical generalised linear models (HGLMs) with spatially correlated Gaussian

    Matern random effects. In particular, by restricting

    􀁑 to the set {0.5, 1.5, 2.5} I

    examine via a simulation study the amount of bias introduced on the fixed effects

    estimates in which the data used to fit the model was generated with different

    values to the aforementioned set. The effect of misspecification was found to be

    minimal.

    By restricting the smoothing parameter,

    􀁑, to the set {0.5, 1.5, 2.5} I utilise the R

    package

    hglm, to develop a procedure (MaternHGLM) for fitting spatial Matern

    HGLMs. In particular, I constructed a hierarchical likelihood (h-likelihood)

    function with given correlation parameters which thus enabled me to Choleski

    decompose the Matern covariance matrix and utilise

    hglm to estimate fixed and

    random effects along with dispersion parameters. Using the above estimated

    parameters I then formed an adjusted profile h-likelihood for the estimation of the

    Matern scaling parameter,

    􀁕, using the Newton-Raphson procedure. Simulation

    studies were carried out to assess the computational efficiency of

    MaternHGLM

    compared to

    spaMM. I found that, on average, MaternHGLM was 136% faster

    than

    spaMM.

    I also analysed two real world datasets using both

    spaMM and MaternHGLM.

    By fixing

    􀁑 at the most appropriate value from the set {0.5, 1.5, 2.5} I examined to

    what extent, if any, did the conclusions drawn differ from those in the original

    study. I found that in general the conclusions were the same, however, on one of

    the datasets

    spaMM’s conclusion didn’t align at all with the original analysis even

    with

    􀁑 estimated from the data.

  • 72. Meng, Xiangli
    et al.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Rebreyend, Pascal
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    How do administrative borders affect accessibility to hospitals? The case of Sweden2018In: International Journal of Health Planning and Management, ISSN 0749-6753, E-ISSN 1099-1751, Vol. 33, no 3Article in journal (Refereed)
    Abstract [en]

    An administrative border might hinder the optimal allocation of a given set of resources by restricting the flow of goods, services, and people. In this paper, we address the question: Do administrative borders lead to poor accessibility to public service? In answering the question, we have examined the case of Sweden and its regional administrative borders and hospital accessibility. We have used detailed data on the Swedish road network, its hospitals, and its geo-coded population. We have assessed the population's spatial accessibility to Swedish hospitals by computing the inhabitants' distance to the nearest hospital. We have also elaborated several scenarios ranging from strongly confining regional borders to no confinements of borders and recomputed the accessibility. Our findings imply that administrative borders are only marginally worsening the accessibility.

  • 73.
    Paidi, Vijay
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Smart parking sensors, technologies and applications for open parking lots: a review2018In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 8, p. 735-741Article in journal (Refereed)
    Abstract [en]

    Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This paper reviews the literature on the usage of smart parking sensors, technologies, applications and evaluate their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this paper suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

  • 74.
    Paidi, Vijay
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Smart Parking Tools Suitability for Open Parking Lots: A Review2018In: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, Madeira, 2018, p. 600-609Conference paper (Refereed)
    Abstract [en]

    Parking a vehicle in traffic dense environments is a common issue in many parts of the world which oftenleads to congestion and environmental pollution. Lack of guidance information to vacant parking spaces isone of the reasons for inefficient parking behaviour. Smart parking sensors and technologies facilitateguidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensorsor technologies are in use for the common open parking lot. This paper reviews the literature on the usage ofsmart parking sensors, technologies, applications and evaluate their suitability to open parking lots. Suitabilitywas made in terms of expenditure and reliability. Magnetometers, ultrasonic sensors and machine vision werefew of the widely used sensors and technologies used in closed parking lots. However, this paper suggests acombination of machine vision, fuzzy logic or multi-agent systems suitable for open parking lots due to lessexpenditure and resistance to varied environmental conditions. No application provided real time parkingoccupancy information of open parking lots, which is a necessity to guide them along the shortest route tofree space. To develop smart parking applications for open parking lots, further research is needed in the fieldsof deep learning.

  • 75. Pan, S
    et al.
    Xiong, Y
    Han, Y
    Zhang, Xingxing
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Xia, L
    Wei, S
    Wu, J
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A study on influential factors of occupant window-opening behavior in an office building in China2018In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 133, p. 41-50Article in journal (Refereed)
    Abstract [en]

    Occupants often perform many types of behavior in buildings to adjust the indoor thermal environment. In these types, opening/closing the windows, often regarded as window-opening behavior, is more commonly observed because of its convenience. It not only improves indoor air quality to satisfy occupants' requirement for indoor thermal comfort but also influences building energy consumption. To learn more about potential factors having effects on occupants' window-opening behavior, a field study was carried out in an office building within a university in Beijing. Window state (open/closed) for a total of 5 windows in 5 offices on the second floor in 285 days (9.5 months) were recorded daily. Potential factors, categorized as environmental and non-environmental ones, were subsequently identified with their impact on window-opening behavior through logistic regression and Pearson correlation approaches. The analytical results show that occupants' window-opening behavior is more strongly correlated to environmental factors, such as indoor and outdoor air temperatures, wind speed, relative humidity, outdoor FM2.5 concentrations, solar radiation, sunshine hours, in which air temperatures dominate the influence. While the non-environmental factors, i.e. seasonal change, time of day and personal preference, also affects the patterns of window-opening probability. This paper provides solid field data on occupant window opening behavior in China, with high resolutions and demonstrates the way in analyzing and predicting the probability of window-opening behavior. Its discussion into the potential impact factors shall be useful for further investigation of the relationship between building energy consumption and window-opening behavior.

  • 76.
    Pourghadiri Esfahani, Mohammad
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Effect analysis of the 2008 charge increase of the nitrogen oxide on "Adoptive NOx Intensity" for Swedish pulp and paper industry2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The aim of this study is to evaluate the effectiveness of the NOx charge increase in 2008 on the behavior of NOx emissions from pulp and paper combustion plants, which are in the fee and refund system (regulated plants) in Sweden. In order to evaluate the potential change in NOx emissions behavior, this study used Difference-in-Differences (DID) approach. This involves a ‘control group’ (unregulated plants) and a ‘treatment group’ (regulated plants). The control group consists of smaller plants which are not included in the fee and refund system. The crudest DID assumption is that the average difference in emissions would have stayed constant over time for the control group and the treatment group if the increase of the fee would not have taken place. The results suggest that the NOx charge increase has had negative effects on NOx emission efficiency ("Adoptive NOx Intensity") for some years after 2008. However the reduction in NOx emission efficiency is inconsistent for the period over 2008-13. The difference between the control group and the treatment group jumps up and down in a way that is difficult to explain by the increase of the fee in 2008. This indicates that the DID assumption for the DID estimator may not be fulfilled. It is concluded that the results given these conditions are not reliable to draw any strong conclusions about the possible effect of the increase of the fee in 2008.

  • 77.
    Prochazka, Jiri
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Zerin, Sonia
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Modeling Hospital Readmissions in Dalarna County, Sweden2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Unplanned hospital readmissions increase health and medical care costs and indicate lower the lower quality of the healthcare services. Hence, predicting patients at risk to be readmitted is of interest. Using administrative data of patients being treated in the medical centers and hospitals in the Dalarna County, Sweden, during 2008 – 2016 two risk prediction models of hospital readmission are built. The first model relies on the logistic regression (LR) approach, predicts correctly 2,648 out of 3,392 observed readmission in the test dataset, reaching a c-statistics of 0.69. The second model is built using random forests (RF) algorithm; correctly predicts 2,183 readmission (out of 3,366) and 13,198 non-readmission events (out of 18,982). The discriminating ability of the best performing RF model (c-statistic 0.60) is comparable to that of the logistic model. Although the discriminating ability of both LR and RF risk prediction models is relatively modest, still these models are capable to identify patients running high risk of hospital readmission. These patients can then be targeted with specific interventions, in order to prevent the readmission, improve patients’ quality of life and reduce health and medical care costs.

  • 78.
    Rahman, Md Rezaur
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Joy, Frank
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Estimation of the causal effect of hospital outlier on patient outcomes: A case study of the hospital and patient care units inDalarna County, Sweden2018Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    An outlier patient is a medical or surgical patient who cannot get admitted to the designated ward or care unit due to the lack of bed occupancy or human resources, and the hospital transfers the patient to another ward or care unit. This study aims to compare and evaluate the outcome of being an outlier patient with a non-outlier patient. Region Dalarna (Lanstinget Dalarna) in Sweden provided data of two hospitals with 158734 cases for the years from 2014 to 2017. This observational casecontrol study used three types of matching techniques to create balanced data sets. Multivariate analysis with logistic regression was used to analyze the outcome of patients regarding mortality rate and unplanned readmission rate. Multiple linear regression was used to perform outcome analysis for the hospital length of stay of the outlier patients. Fisher’s exact test was used to evaluate the significance of mortality rate and unplanned readmission in 30 days. For the patient’s length of stay, the study used two independent t-test. Medical outlier patients did not get affected regarding unplanned readmission. In case of mortality, two of our matched datasets showed outlier patients did not have different mortality rate than non-outlier patients; only one matched dataset showed significance for mortality in case of outlier patients compared to the non-outlier patients. However, outlier patients had a significantly shorter duration of hospital stay than non-outlier patients in all three matched dataset.

  • 79.
    Rajasekaran, Prabakaran
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A statistical data analysis approach to Energy Data: A Case Study in Building Performance Analysis of Thermal Energy Loss2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The usage of energy in buildings is higher in Sweden’s cold climate, most buildings consume

    significant energy to heat buildings during the winter and cool the buildings during the summer, using

    the district heat and electricity. The building energy loss is the difference between indoor and outdoor

    temperatures. When the temperature difference is higher during the heating season (winter), there is

    a need to balance the indoor temperature. More power supply is needed to warm the indoor area. In

    order to find which factor has a significant impact on the building, heat loss and gain can be used as

    variables in the multiple linear regression model to analyze the building energy performance. In order

    to build the multiple – linear regression model which is used, the measured parameters for the building

    and some of the data should be calculated, such as solar heat gain through windows from the (North,

    East, West &South) building. The heat loss to the ground is based on constructed material, the thermal

    conductivity of the material, indoor and outdoor temperature, and steady state ground heat transfer

    coefficient. After building the model, an analysis of the fit model test is needed, to investigate if the

    coefficients are properly estimated. Based on this analysis, we can see the comparison between

    renovated building and non renovated building significant impact of the energy consumption for

    given the energy and financial investment.

  • 80.
    Rebreyend, Pascal
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Lemarchand, Laurent
    Massé, Damien
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Multiobjective Optimization for Multimode Transportation Problems2018In: Advances in Operations Research, ISSN 1687-9147, E-ISSN 1687-9155, article id 8720643Article in journal (Refereed)
    Abstract [en]

    We propose modelling for a facilities localization problem in the context of multimode transportation. The applicative goal is to locate service facilities such as schools or hospitals while optimizing the different transportation modes to these facilities. We formalize the School Problem and solve it first exactly using an adapted -constraint multiobjective method. Because of the size of the instances considered, we have also explored the use of heuristic methods based on evolutionary multiobjective frameworks, namely, NSGA2 and a modified version of PAES. Those methods are mixed with an original local search technique to provide better results. Numerical comparisons of solutions sets quality are made using the hypervolume metric. Based on the results for test-cases that can be solved exactly, efficient implementation for PAES and NSGA2 allows execution times comparison for large instances. Results show good performances for the heuristic approaches as compared to the exact algorithm for small test-cases. Approximate methods present a scalable behavior on largest problem instances. A master/slave parallelization scheme also helps to reduce execution times significantly for the modified PAES approach.

  • 81.
    Rudholm, Niklas
    et al.
    Dalarna University, School of Technology and Business Studies, Economics. HUI Research.
    Li, Yujiao
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    How Does Big-Box Entry Affect Labor Productivity in Durable Goods Retailing? A Synthetic Control Approach. How Does Big-Box Entry Affect Labor Productivity in Durable Goods Retailing? A Synthetic Control Approach2018Report (Other academic)
    Abstract [en]

    Using  data  from  2001–2012,  the  effects  of  IKEA  entry  in  four  Swedish  municipalities,   2004–2007,   on   labor   productivity   in   durable   goods   retailing   is   investigated  using  synthetic  control  methods.  We contribute  to  the  literature  on  synthetic   control   methods   by considering   parametric   specifications   of   the   intervention effect, which in our case arguably improves the likelihood of identifying the intervention effect of IKEA entry on labor productivity. As inference relies on a single  treated  observational  unit  (i.e.,  a  single  IKEA-entry  municipality),  statistical  testing  is  a  challenge,  and  randomization  and  replication  for  inference  is  done  with  regard to the pool of control municipalities. Our results indicate that in three out of four  entry  municipalities,  labor  productivity  increased  more  than  in  their  synthetic  counterparts after IKEA entry, and that the size of the positive effect is related to the size of the new IKEA relative to the size of the existing durable goods retail sector in the entry municipalities, with larger positive effects found in municipalities where the new IKEA was large relative to the existing durable goods retail market.

  • 82.
    Saqlain, Murshid
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Alam, Moudud
    Dalarna University, School of Technology and Business Studies, Statistics.
    Brandt, Daniel
    Dalarna University, School of Technology and Business Studies, Human Geography.
    Rönnegård, Lars
    Dalarna University, School of Technology and Business Studies, Statistics.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Stochastic differential equations modelling of levodopa concentration in patients with Parkinson's disease2018Conference paper (Other academic)
    Abstract [en]

    The purpose of this study is to investigate a pharmacokinetic model of levodopa concentration in patients with Parkinson's disease by introducing stochasticity so that inter-individual variability may be separated into measurement and system noise. It also aims to investigate whether the stochastic differential equations (SDE) model provide better fits than its ordinary differential equations (ODE) counterpart, by using a real data set. Westin et al. developed a pharmacokinetic-pharmacodynamic model for duodenal levodopa infusion described by four ODEs, the first three of which define the pharmacokinetic model. In this study, system noise variables are added to the aforementioned first three equations through a standard Wiener process, also known as Brownian motion. The R package PSM for mixed-effects models is used on data from previous studies for modelling levodopa concentration and parameter estimation. First, the diffusion scale parameter, σ, and bioavailability are estimated with the SDE model. Second, σ is fixed to integer values between 1 and 5, and bioavailability is estimated. Cross-validation is performed to determine whether the SDE based model explains the observed data better or not by comparingthe average root mean squared errors (RMSE) of predicted levodopa concentration. Both ODE and SDE models estimated bioavailability to be about 88%. The SDE model converged at different values of σ that were signicantly different from zero while estimating bioavailability to be about 88%. The average RMSE for the ODE model wasfound to be 0.2980, and the lowest average RMSE for the SDE model was 0.2748 when σ was xed to 4. Both models estimated similar values for bioavailability, and the non-zero σ estimate implies that the inter-individual variability may be separated. However, the improvement in the predictive performance of the SDE model turned out to be rather small, compared to the ODE model.

  • 83.
    Svenson, Kristin
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A Microdata Analysis Approach to Transport Infrastructure Maintenance2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Maintenance of transport infrastructure assets is widely advocated as the key in minimizing current and future costs of the transportation network. While effective maintenance decisions are often a result of engineering skills and practical knowledge, efficient decisions must also account for the net result over an asset's life-cycle. One essential aspect in the long term perspective of transport infrastructure maintenance is to proactively estimate maintenance needs. In dealing with immediate maintenance actions, support tools that can prioritize potential maintenance candidates are important to obtain an efficient maintenance strategy.

    This dissertation consists of five individual research papers presenting a microdata analysis approach to transport infrastructure maintenance. Microdata analysis is a multidisciplinary field in which large quantities of data is collected, analyzed, and interpreted to improve decision-making. Increased access to transport infrastructure data enables a deeper understanding of causal effects and a possibility to make predictions of future outcomes. The microdata analysis approach covers the complete process from data collection to actual decisions and is therefore well suited for the task of improving efficiency in transport infrastructure maintenance.

    Statistical modeling was the selected analysis method in this dissertation and provided solutions to the different problems presented in each of the five papers. In Paper I, a time-to-event model was used to estimate remaining road pavement lifetimes in Sweden. In Paper II, an extension of the model in Paper I assessed the impact of latent variables on road lifetimes; displaying the sections in a road network that are weaker due to e.g. subsoil conditions or undetected heavy traffic. The study in Paper III incorporated a probabilistic parametric distribution as a representation of road lifetimes into an equation for the marginal cost of road wear. Differentiated road wear marginal costs for heavy and light vehicles are an important information basis for decisions regarding vehicle miles traveled (VMT) taxation policies.

    In Paper IV, a distribution based clustering method was used to distinguish between road segments that are deteriorating and road segments that have a stationary road condition. Within railway networks, temporary speed restrictions are often imposed because of maintenance and must be addressed in order to keep punctuality. The study in Paper V evaluated the empirical effect on running time of speed restrictions on a Norwegian railway line using a generalized linear mixed model.

  • 84.
    Svenson, Kristin
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    McRobbie, S.
    Alam, Moudud
    Dalarna University, School of Technology and Business Studies, Statistics.
    Detecting road pavement deterioration with finite mixture models2019In: The international journal of pavement engineering, ISSN 1029-8436, E-ISSN 1477-268X, Vol. 20, no 4, p. 458-465Article in journal (Refereed)
    Abstract [en]

    Budget restrictions often limit the number of possible maintenance activities in a road network each year. To effectively allocate resources, the rate of road pavement deterioration is of great importance. If two maintenance candidates have an equivalent condition, it is reasonable to maintain the segment with the highest deterioration rate first. To identify such segments, finite mixture models were applied to road condition data from a part of the M4 highway in England. Assuming that data originates from two different normal distributions – defined as a ‘change’ distribution and an ‘unchanged’ distribution – all road segments were classified into one of the groups. Comparisons with known measurement errors and maintenance records showed that segments in the unchanged group had a stationary road condition. Segments classified into the change group showed either a rapid deterioration, improvement in condition because of previous maintenance or unusual measurement errors. Together with additional information from maintenance records, finite mixture models can identify segments with the most rapid deterioration rate, and contribute to more efficient maintenance decisions.

  • 85.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Automating levodopa dosing schedules for Parkinson’s disease2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Parkinson’s disease (PD) is the second most common neurodegenerative disease. Levodopa is mainly used to manage the motor symptoms of PD. However, disease progression and long-term use of levodopa cause reduced medication efficacy and side effects. When that happens, precise individualized dosing schedules are required.

    This doctoral thesis in the field of Micro-data analysis introduces an end-to-end solution for the automation of the pharmacological management of PD with levodopa, and offers some insight on levodopa pharmacodynamics. For that purpose, an algorithm that derives objective ratings for the patients’ motor function through wearable sensors is introduced, a method to construct individual patient profiles is developed, and two dosing algorithms for oral and intestinal administration of levodopa are presented. Data from five different sources were used to develop the methods and evaluate the performance of the proposed algorithms.

    The dose automation algorithms can work both with clinical and objective ratings (through wearable devices), and their application was evaluated against dosing adjustments from movement disorders experts. Both dosing algorithms showed promise and their dosing suggestions were similar to those of the clinicians.

    The objective ratings algorithm had good test-retest reliability and its application during a clinical study was successful. Furthermore, the method of fitting individual patient models was robust and worked well with the objective ratings algorithm. Finally, a study was carried out that showed that about half the patients on levodopa treatment show reduced response during the afternoon hours, pointing to the need for more precise modelling of levodopa pharmacodynamics.

  • 86.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Optimizing levodopa dosing routines for Parkinson’s disease2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis in the field of microdata analysis aims to introduce dose optimizing algorithms for the pharmacological management of Parkinson’s disease (PD). PD is a neurodegenerative disease that mostly affects the motor functions of the patients and it is characterized as a movement disorder. The core symptoms of PD are: bradykinesia, postural instability, rigidity, and tremor. There is no cure for PD and the use of levodopa to manage the core symptoms is considered the gold standard. However, long term use of levodopa causes reduced medication efficacy, and side effects, such as dyskinesia, which can also be attributed to overmedication. When that happens precise individualized dosing schedules are required. The goal of this thesis is to examine if algorithmic methods can be used to find dosing schedules that treat PD symptoms and minimize manifestation of side effects. Data from three different sources were used for that purpose: data from a clinical study in Uppsala University hospital in 2015, patient admission chart data from Uppsala University hospital during 2011-2015, and data from a clinical study in Gothenburg University during 2016-2017. The data were used to develop the methods and evaluate the performance of the proposed algorithms.The first algorithm that was developed was a sensor-based method that derives objective measurements (ratings) of PD motor states. The construction of the sensor index was based on subjective ratings of patients’ motor functions made by three movement disorder experts. This sensor-based method was used when deriving algorithmic dosing schedules. Afterwards, a method that uses medication information and ratings of the patients’ motor states to fit individual patient models was developed. This method uses mathematical optimization to individualize specific parameters of dose-effects models for levodopa intake, through minimizing the distance between motor state ratings and dose-effect curves. Finally, two different dose optimization algorithms were developed and evaluated, that had as input the individual patient models. The first algorithm was specific to continuous infusion of levodopa treatment, where the patient’s state was set to a specific target value and the algorithm made dosing adjustments to keep that patients motor functions on that state. The second algorithm concerned oral administration of microtables of levodopa. The ambition with this algorithm was that the suggested doses would find the right balance between treating the core symptoms of PD and, at the same time, minimizing the side effects of long term levodopa use, mainly dyskinesia. Motor state ratings for this study were obtained through the sensor index. Both algorithms followed a principle of deriving a morning dose and a maintenance dose for the patients, with maintenance dose being an infusion rate for the first algorithm, and oral administration doses at specific time points for the second algorithm.The results showed that the sensor-based index had good test-retest reliability, sensitivity to levodopa treatment, and ability to make predictions in unseen parts of the dataset. The dosing algorithm for continuous infusion of levodopa had a good ability to suggest an optimal infusion rating for the patients, but consistently suggested lower morning dose than what the treating personnel prescribed. The dosing algorithm for oral administration of levodopa showed great agreement with the treating personnel’s prescriptions, both in terms of morning and maintenance dose. Moreover, when evaluating the oral medication algorithm, it was clear that the sensor index ratings could be used for building patient specific models.

  • 87.
    Thomas, Ilias
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Alam, Moudud
    Dalarna University, School of Technology and Business Studies, Statistics.
    Bergquist, Filip
    Johansson, Dongni
    Memedi, Mevludin
    Nyholm, Dag
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease: a first experience2019In: Journal of Neurology, ISSN 0340-5354, E-ISSN 1432-1459, Vol. 266, no 3, p. 651-658Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE: Dosing schedules for oral levodopa in advanced stages of Parkinson's disease (PD) require careful tailoring to fit the needs of each patient. This study proposes a dosing algorithm for oral administration of levodopa and evaluates its integration into a sensor-based dosing system (SBDS).

    MATERIALS AND METHODS: In collaboration with two movement disorder experts a knowledge-driven, simulation based algorithm was designed and integrated into a SBDS. The SBDS uses data from wearable sensors to fit individual patient models, which are then used as input to the dosing algorithm. To access the feasibility of using the SBDS in clinical practice its performance was evaluated during a clinical experiment where dosing optimization of oral levodopa was explored. The supervising neurologist made dosing adjustments based on data from the Parkinson's KinetiGraph™ (PKG) that the patients wore for a week in a free living setting. The dosing suggestions of the SBDS were compared with the PKG-guided adjustments.

    RESULTS: The SBDS maintenance and morning dosing suggestions had a Pearson's correlation of 0.80 and 0.95 (with mean relative errors of 21% and 12.5%), to the PKG-guided dosing adjustments. Paired t test indicated no statistical differences between the algorithmic suggestions and the clinician's adjustments.

    CONCLUSION: This study shows that it is possible to use algorithmic sensor-based dosing adjustments to optimize treatment with oral medication for PD patients.

  • 88.
    Thomas, Ilias
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Alam, Moudud
    Dalarna University, School of Technology and Business Studies, Statistics.
    Nyholm, Dag
    Senek, Marina
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Individual dose-response models for levodopa infusion dose optimization2018In: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 112, p. 137-142Article in journal (Refereed)
    Abstract [en]

    Background and Objective

    To achieve optimal effect with continuous infusion treatment in Parkinson’s disease (PD), the individual doses (morning dose and continuous infusion rate) are titrated by trained medical personnel. This study describes an algorithmic method to derive optimized dosing suggestions for infusion treatment of PD, by fitting individual dose-response models. The feasibility of the proposed method was investigated using patient chart data.

    Methods

    Patient records were collected at Uppsala University hospital which provided dosing information and dose-response evaluations. Mathematical optimization was used to fit individual patient models using the records’ information, by minimizing an objective function. The individual models were passed to a dose optimization algorithm, which derived an optimized dosing suggestion for each patient model.

    Results

    Using data from a single day’s admission the algorithm showed great ability to fit appropriate individual patient models and derive optimized doses. The infusion rate dosing suggestions had 0.88 correlation and 10% absolute mean relative error compared to the optimal doses as determined by the hospital’s treating team. The morning dose suggestions were consistency lower that the optimal morning doses, which could be attributed to different dosing strategies and/or lack of on-off evaluations in the morning.

    Conclusion

    The proposed method showed promise and could be applied in clinical practice, to provide the hospital personnel with additional information when making dose adjustment decisions.

  • 89.
    Thomas, Ilias
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Bergquist, Filip
    Gothenburg University.
    Constantinescu, Radu
    Gothenburg University.
    Nyholm, Dag
    Dept. of Neuroscience, Neurology, Uppsala University.
    Senek, Marina
    Dept. of Neuroscience, Neurology, Uppsala University.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering. Informatics, School of Business, Örebro University.
    Using measurements from wearable sensors for automatic scoring of Parkinson's disease motor states: Results from 7 patients2017In: Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, IEEE, 2017, p. 131-134Conference paper (Refereed)
    Abstract [en]

    The objective of this study was to investigate the validity of an objective gait measure for assessment of different motor states of advanced Parkinson's disease (PD) patients. Seven PD patients performed a gait task up to 15 times while wearing sensors on their upper and lower limbs. Each task was performed at specific points during a test day, following a single dose of levodopa-carbidopa. At the time of the tasks the patients were video recorded and three movement disorder experts rated their motor function on three clinical scales: a treatment response scale (TRS) that ranged from −3 (very bradykinetic) to 0 (ON) to +3 (very dyskinetic), a dyskinesia score that ranged from 0 (no dyskinesia) to 4 (extreme dyskinesia), and a bradykinesia score that ranged from 0 (no bradykinesia) to 4 (extreme bradykinesia). Raw accelerometer and gyroscope data of the sensors were processed and analyzed with time series analysis methods to extract features. The utilized features quantified separate limb movements as well as movement symmetries between the limbs. The features were processed with principal component analysis and the components were used as predictors for separate support vector machine (SVM) models for each of the three scales. The performance of each model was evaluated in a leave-one-patient out setting where the observations of a single patient were used as the testing set and the observations of the other 6 patients as the training set. Root mean square error (RMSE) and correlation coefficients for the predictions showed a good ability of the models to map the sensor data into the rating scales. There were strong correlations between the SVM models and the mean ratings of TRS (0.79; RMSE=0.70), bradykinesia score (0.79; RMSE=0.47), and bradykinesia score (0.78; RMSE=0.46). The results presented in this paper indicate that the use of wearable sensors when performing gait tasks can generate measurements that have a good correlation to subjective expert assessments.

  • 90.
    Thomas, Ilias
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Memedi, Mevludin
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nyholm, Dag
    The effect of continuous levodopa treatment during the afternoon hours2019In: Acta Neurologica Scandinavica, ISSN 0001-6314, E-ISSN 1600-0404, Vol. 139, no 1, p. 70-75Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE: The aim of this retrospective study was to investigate if patients with PD, who are treated with levodopa-carbidopa intestinal gel (LCIG), clinically worsen during the afternoon hours and if so, to evaluate whether this occurs in all LCIG-treated patients or in a sub-group of patients.

    METHODS: Three published studies were identified and included in the analysis. All studies provided individual response data assessed on the treatment response scale (TRS) and patients were treated with continuous LCIG. Ninety-eight patients from the three studies fulfilled the criteria. T-tests were performed to find differences on the TRS values between the morning and the afternoon hours, linear mixed effect models were fitted on the afternoon hours' evaluations to find trends of wearing-off, and patients were classified into three TRS categories (meaningful increase in TRS, meaningful decrease in TRS, non -meaningful increase or decrease).

    RESULTS: In all three studies significant statistical differences were found between the morning TRS values and the afternoon TRS values (p-value <= 0.001 in all studies). The linear mixed effect models had significant negative coefficients for time in two studies, and 48 out of 98 patients (49%) showed a meaningful decrease of TRS during the afternoon hours.

    CONCLUSION: The results from all studies were consistent, both in the proportion of patients in the three groups and the value of TRS decrease in the afternoon hours. Based on these findings there seems to be a group of patients with predictable "off" behavior in the later parts of the day. This article is protected by copyright. All rights reserved.

  • 91.
    Thomas, Ilias
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Alam, Moudud
    Dalarna University, School of Technology and Business Studies, Statistics.
    Bergquist, F.
    Nyholm, D.
    Senek, M.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A treatment–response index from wearable sensors for quantifying Parkinson's disease motor states2018In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, no 5, p. 1341-1349Article in journal (Refereed)
    Abstract [en]

    The goal of this study was to develop an algorithm that automatically quantifies motor states (off,on,dyskinesia) in Parkinson's disease (PD), based on accelerometry during a hand pronation-supination test. Clinician's ratings using the Treatment Response Scale (TRS), ranging from -3 (very Off) to 0 (On) to +3 (very dyskinetic), was used as target. For that purpose, 19 participants with advanced PD and 22 healthy persons were recruited in a single center open label clinical trial in Uppsala, Sweden. The trial consisted of single levodopa dose experiments for the people with PD (PwP), where participants were asked to perform standardized wrist rotation tests, using each hand, before and at pre-specified time points after the dose. The participants used wrist sensors containing a 3D accelerometer and gyroscope. Features to quantify the level, variation and asymmetry of the sensor signals, three-level Discrete Wavelet Transform features and approximate entropy measures were extracted from the sensors data. At the time of the tests, the PwP were video recorded. Three movement disorder specialists rated the participants’ state on the TRS scale. A Treatment Response Index from Sensors (TRIS) was constructed to quantify the motor states based on the wrist rotation tests. Different machine learning algorithms were evaluated to map the features derived from the sensor data to the ratings provided by the three specialists. Results from cross validation, both in 10-fold and a leave-one-individual out setting, showed good predictive power of a support vector machine model and high correlation to the TRS scale. Values at the end tails of the TRS scale were under and over predicted due to the lack of observations at those values but the model managed to accurately capture the dose - effect profiles of the patients. In addition, the TRIS had good test-retest reliability on the baseline levels of the PD participants (Intraclass correlation coefficient of 0.83) and reasonable sensitivity to levodopa treatment (0.33 for the TRIS). For a series of test occasions the proposed algorithms provided dose - effect time profiles for participants with PD, which could be useful during therapy individualization of people suffering from advanced PD

  • 92.
    Vasile, Manea
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Do administrative borders affect the long-term population dynamics?2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    How borders affect human activities like economic life has been a frequent research topic during the last decades. A widespread view is that local societies are affected by the proximity of their borders in one way or another. Within a specific country, research on border issues often has a center – periphery perspective, problematizing sub-national administrative decision-making as well as local and regional economic development. Besides international migration, it is not thoroughly investigated how demographical processes are affected by borders, although population change is of crucial importance for economic development. The aim in this study is therefore to analyze how local population changes in localities close to administrative borders are affected by local population change in the center. With this purpose, the causal time-space relationship is estimated. In this study, for a regional administrative unit (county), the population change in the smallest available administrative units (parishes) closely located to a county border are compared to the population change in those parishes located in the center in a county. This study builds on a long term time perspective and uses information on population totals in parishes in Sweden between 1810 and 2000. The long-term time perspective is needed because population change normally is a slow process. The findings show that there is a border effect and, on average, the population of Sweden tends to migrate from border locations to center locations within administrative borders. However, this process is region-dependent and is opposite in the southern counties, where the population migrates from the central towards the border locations, which most often are located at the coast.

  • 93.
    Wahab, Nor-Ul
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Evaluation of Supervised Machine LearningAlgorithms for Detecting Anomalies in Vehicle’s Off-Board Sensor Data2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    A diesel particulate filter (DPF) is designed to physically remove diesel particulate matter or soot from the exhaust gas of a diesel engine. Frequently replacing DPF is a waste of resource and waiting for full utilization is risky and very costly, so, what is the optimal time/milage to change DPF? Answering this question is very difficult without knowing when the DPF is changed in a vehicle.

    We are finding the answer with supervised machine learning algorithms for detecting anomalies in vehicles off-board sensor data (operational data of vehicles). Filter change is considered an anomaly because it is rare as compared to normal data.

    Non-sequential machine learning algorithms for anomaly detection like oneclass support vector machine (OC-SVM), k-nearest neighbor (K-NN), and random forest (RF) are applied for the first time on DPF dataset. The dataset is unbalanced, and accuracy is found misleading as a performance measure for the algorithms. Precision, recall, and F1-score are found good measure for the performance of the machine learning algorithms when the data is unbalanced. RF gave highest F1-score of 0.55 than K-NN (0.52) and OCSVM (0.51). It means that RF perform better than K-NN and OC-SVM but after further investigation it is concluded that the results are not satisfactory. However, a sequential approach should have been tried which could yield better result.

  • 94. Wei, Yixuan
    et al.
    Zhang, Xingxing
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Shi, Yong
    Xia, Liang
    Pan, Song
    Wu, Jinshun
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Zhao, Xiaoyun
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A review of data-driven approaches for prediction and classification of building energy consumption2018In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 82, no 1, p. 1027-1047Article in journal (Refereed)
    Abstract [en]

    A recent surge of interest in building energy consumption has generated a tremendous amount of energy data, which boosts the data-driven algorithms for broad application throughout the building industry. This article reviews the prevailing data-driven approaches used in building energy analysis under different archetypes and granularities, including those methods for prediction (artificial neural networks, support vector machines, statistical regression, decision tree and genetic algorithm) and those methods for classification (K-mean clustering, self-organizing map and hierarchy clustering). The review results demonstrate that the data-driven approaches have well addressed a large variety of building energy related applications, such as load forecasting and prediction, energy pattern profiling, regional energy-consumption mapping, benchmarking for building stocks, global retrofit strategies and guideline making etc. Significantly, this review refines a few key tasks for modification of the data-driven approaches in the context of application to building energy analysis. The conclusions drawn in this review could facilitate future micro-scale changes of energy use for a particular building through the appropriate retrofit and the inclusion of renewable energy technologies. It also paves an avenue to explore potential in macro-scale energy-reduction with consideration of customer demands. All these will be useful to establish a better long-term strategy for urban sustainability.

  • 95.
    Yang, Bowen
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Ranking hotels for recommendation via generalized linear mixed model and Box-Cox model: A case of Stockholm rating data from booking.com2014Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis is aimed at recommending suitable hotels to the customers using the data collected from the website booking.com. In this thesis, data from Stockholm is chosen as an example, and statistical modeling is applied. We propose recommended hotels based on their rankings in terms of the scores of the hotels. The ranking score is derived by using eneralized linear mixed models. Box-Cox transformation is applied further to improve the previous analysis. Separate group analysis indicates that the ranks between different reviewer groups are significantly different. Model evaluation is executed via Cross-validation method by calculating the classification accuracies for all models. The best model is found based on theclassification accuracy, and we recommend the top 10, top 15 and top 20 hotels from the best model in this thesis.

  • 96.
    Zhang, Fan
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A review on electricity price forecasting using neural network based models2018Report (Other (popular science, discussion, etc.))
  • 97.
    Zhang, Fan
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Short term electricity price forecasting using CatBoost and bidirectional long short term memory neural network2018Report (Other (popular science, discussion, etc.))
  • 98.
    Zhang, Fan
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Wang, X.
    Lu, M.
    Construction site accident analysis using text mining and natural language processing techniques2019In: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 99, p. 238-248Article in journal (Refereed)
    Abstract [en]

    Workplace safety is a major concern in many countries. Among various industries, construction sector is identified as the most hazardous work place. Construction accidents not only cause human sufferings but also result in huge financial loss. To prevent reoccurrence of similar accidents in the future and make scientific risk control plans, analysis of accidents is essential. In construction industry, fatality and catastrophe investigation summary reports are available for the past accidents. In this study, text mining and natural language process (NLP) techniques are applied to analyze the construction accident reports. To be more specific, five baseline models, support vector machine (SVM), linear regression (LR), K-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB) and an ensemble model are proposed to classify the causes of the accidents. Besides, Sequential Quadratic Programming (SQP) algorithm is utilized to optimize weight of each classifier involved in the ensemble model. Experiment results show that the optimized ensemble model outperforms rest models considered in this study in terms of average weighted F1 score. The result also shows that the proposed approach is more robust to cases of low support. Moreover, an unsupervised chunking approach is proposed to extract common objects which cause the accidents based on grammar rules identified in the reports. As harmful objects are one of the major factors leading to construction accidents, identifying such objects is extremely helpful to mitigate potential risks. Certain limitations of the proposed methods are discussed and suggestions and future improvements are provided.

  • 99.
    Zhang, Xingxing
    et al.
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Lovati, Marco
    Vigna, Ilaria
    Widén, Joakim
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Gál, Csilla V
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Feng, Tao
    A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions2018In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 230, p. 1034-1056Article in journal (Refereed)
    Abstract [en]

    The emergence of renewable-energy-source (RES) envelope solutions, building retrofit requirements and advanced energy technologies brought about challenges to the existing paradigm of urban energy systems. It is envisioned that the building cluster approach—that can maximize the synergies of RES harvesting, building performance, and distributed energy management—will deliver the breakthrough to these challenges. Thus, this paper aims to critically review urban energy systems at the cluster level that incorporate building integrated RES solutions. We begin with defining cluster approach and the associated boundaries. Several factors influencing energy planning at cluster scale are identified, while the most important ones are discussed in detail. The closely reviewed factors include RES envelope solutions, solar energy potential, density of buildings, energy demand, integrated cluster-scale energy systems and energy hub. The examined categories of RES envelope solutions are (i) the solar power, (ii) the solar thermal and (iii) the energy-efficient ones, out of which solar energy is the most prevalent RES. As a result, methods assessing the solar energy potentials of building envelopes are reviewed in detail. Building density and the associated energy use are also identified as key factors since they affect the type and the energy harvesting potentials of RES envelopes. Modelling techniques for building energy demand at cluster level and their coupling with complex integrated energy systems or an energy hub are reviewed in a comprehensive way. In addition, the paper discusses control and operational methods as well as related optimization algorithms for the energy hub concept. Based on the findings of the review, we put forward a matrix of recommendations for cluster-level energy system simulations aiming to maximize the direct and indirect benefits of RES envelope solutions. By reviewing key factors and modelling approaches for characterizing RES-envelope-solutions-based urban energy systems at cluster level, this paper hopes to foster the transition towards more sustainable urban energy systems.

  • 100.
    Zhao, Xiaoyun
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Government vs Market in Sustainable Residential Development?: Microdata analysis of car travel, CO2 emission and residence location2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Increasing car usage and travel demands between residential locations and destinations in order to fulfill the various needs of residents is a primary cause of CO2 emissions. To win the battle against climate change, a better understanding of the question relating to which urban residential form may most effectively mitigate the CO2 emissions is the key pathway.

    This dissertation is concerned with the above problem and it mainly considers three objectives in providing insights on answering the question. The first objective is to comprehensively and microscopically understand intra-urban car travel behavior. The second objective is to estimate the induced CO2 emissions from daily intra-urban car travel and to ex-ante evaluate residential plans. The third objective is to assess whether the governmental sustainable residential development objective is aligned with the objectives of the estate market actors. To explore the research questions related to the objectives, a microdata analysis process (data collection, data assessment and transformation, data storage, data analysis and decision-making) is applied and is found essential in gaining access to key variables in exploring the answer of a preferable urban form. The dissertation offers many new solutions to various technical aspects through a microdata analysis process.

    The primary contribution of this dissertation is that it outlines an operational model that comprehensively integrates the investors’ investment strategy, the residents’ choice behavior, and the governmental sustainability objective in the interest of making an ex-ante assessment of residential plans. This ex-ante assessment provides decision-support in sustainable residential development at foremost local level.

    The first finding from the implementation of the model on the case study is that the market actors’ objectives are, in general, aligned with the government’s sustainable residential development objective. The second finding indicates that re-shaping the urban form into a compact city is preferable in mitigating CO2 emissions, in spite of the fact that the case city is of a polycentric urban form. These findings provide support for those advocating the compact city as the ideal for sustainable residential development, and also provide foresight on settling the answer to the preferred re-shaping of urban forms in climate change.

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