Proseduraalisen tietomallintamisen käyttöönotto kaupunkisuunnittelussa

This thesis examines procedural modeling as a tool for urban plan creation. Procedural modeling historically has been used for 3D visualization of natural features, but with the release of the soft-ware CityEngine in 2008 the technology can easily be adopted also in problem domains dealing with urban environments. The study begins with a requirement analysis conducted to explore the needs urban planning imposes on the technology, based on which a functional procedural modeling production system is built using the CityEngine platform and its Computer Generated Architecture (CGA) scripting language. A solution is presented to the problem of control in procedural generation methods by introducing the concept of a selectable “Level of Control” and how its implementation in the produced system enables the planner to flexibly assume the necessary amount of control over the generated model. The finished product is then compared against the presented requirements of accuracy, efficiency, ease of use, high visual qualities, and advanced analytical capabilities. The efficiency of the system measured as the ratio between user interactions (mouse clicks and keystrokes) and modeling output in the setting of the assessment is found out to be two to three times greater than the efficiency of a more established manual modeling software. The technology as demonstrated through the produced system is concluded to be especially suitable for preliminary land use studies estimating the building potentials of extensive land areas. Directions for future research with potential to expand the applicability of the technology are discussed.Tässä diplomityössä tutkitaan proseduraalista mallintamista kaupunkisuunnittelun työvälineenä. Proseduraalista mallintamista on historiallisesti käytetty luonnonmuotojen 3D-visualisoimiseen, mutta vuonna 2008 julkaistu CityEngine-ohjelma mahdollistaa teknologian helpon käyttöönoton myös rakennettua ympäristöä koskevissa aihepiireissä. Tutkielma alkaa analyysillä kaupunkisuunnittelun teknologiaan kohdistamista vaatimuksista, joiden perusteella rakennetaan CityEngineen ja sen Computer Generated Architecture (CGA) ohjel-mointikieleen perustuva proseduraalinen mallinnusjärjestelmä. Ratkaisuna proseduraaliseen mallintamiseen liittyvään kontrollin problematiikkaan esitellään käsite valittavasta ”kontrollitasosta”, ja kuinka sen implementaatio toteutetussa järjestelmässä mahdollistaa suunnittelijan ottaa joustavasti tarpeellisen määrän kontrollia generoitavan mallin suhteen. Valmista tuotetta verrataan esitettyihin tarkkuuden, tehokkuuden, käytön helppouden, korkealaatuisen visuaalisuuden, sekä kehittyneen analytiikan vaatimuksiin. Järjestelmän tehokkuus mitattuna käyttäjäinteraktioiden (hiiren klikkaukset ja näppäimistön painallukset) ja tuotetun mallin suhteena mittauksen asetelmassa on kahdesta kolmeen kertaa suurempi kuin vakiintuneemman manuaalisen mallinnusohjelman tehokkuus. Proseduraalisen mallintamisen, sellaisena kuin se tuotetussa järjestelmässä on implementoitu, todetaan olevan erityisen sopiva alustavien rakentamisen määrää laajoille alueille haarukoivien maankäyttötarkastelujen tuottamiseen. Työn lopuksi käsitellään teknologian käyttöaluetta laajentavia tutkimussuuntia

The Intersection-Validation Method for Evaluating Bayesian Network Structure Learning Without Ground Truth

Structure learning algorithms for Bayesian networks are typically evaluated by examining how accurately they recover the correct structure, given data sampled from a benchmark network. A popular metric for the evaluation is the structural Hamming distance. For real-world data there is no ground truth to compare the learned structures against. Thus, to use such data, one has been limited to evaluating the algorithms' predictive performance on separate test data or via cross-validation. The predictive performance, however, depends on the parameters of the network, for which some fixed values can be used or which can be marginalized over to obtain the posterior predictive distribution using some parameter prior. Predictive performance therefore has an intricate relationship to structural accuracy -- the two do not always perfectly mirror each other. We present intersection-validation, a method for evaluating structure learning without ground truth. The input to the method is a dataset and a set of compared algorithms. First, a partial structure, called the agreement graph, is constructed consisting of the features that the algorithms agree on given the dataset. Then, the algorithms are evaluated against the agreement graph on subsamples of the data, using a variant of the structural Hamming distance. To test the method's validity we define a set of algorithms that return a score maximizing structure using various scoring functions in combination with an exact search algorithm. Given data sampled from benchmark networks, we compare the results of the method to those obtained through direct evaluation against the ground truth structure. Specifically, we consider whether the rankings for the algorithms determined by the distances measured using the two methods conform with each other, and whether there is a strong positive correlation between the two distances. We find that across the experiments the method gives a correct ranking for two algorithms (relative to each other) with an accuracy of approximately 0.9, including when the method is applied onto a set of only two algorithms. The Pearson correlations between the distances are fairly strong but vary to a great extent, depending on the benchmark network, the amount of data given as input to intersection-validation and the sample size at which the distances are measured. We also attempt to predict when the method produces accurate results from information available in situations where the method would be used in practice, namely, without knowledge of the ground truth. The results from these experiments indicate that although some predictors can be found they do not have the same strength in all instances of use of the method. Finally, to illustrate the uses for the method we apply it on a number of real-world datasets in order to study the effect of structure priors on learning

Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth

To compare learning algorithms that differ by the adopted statistical paradigm, model class, or search heuristic, it is common to evaluate the performance on training data of varying size. Measuring the performance is straightforward if the data are generated from a known model, the ground truth. However, when the study concerns real-world data, the current methodology is limited to estimating predictive performance, typically by cross-validation. This work introduces a method to compare algorithms’ ability to learn the model structure, assuming no ground truth is given. The idea is to identify a partial structure on which the algorithms agree, and measure the performance in relation to that structure on subsamples of the data. The method is instantiated to structure learning in Bayesian networks, measuring the performance by the structural Hamming distance. It is tested using benchmark ground truth networks and algorithms that maximize various scoring functions. The results show that the method can produce evaluation outcomes that are close to those one would obtain if the ground truth was available.Peer reviewe

On Structure Priors for Learning Bayesian Networks

Peer reviewe

Layering-MCMC for Structure Learning in Bayesian Networks

Bayesian inference of the Bayesian network structure requires averaging over all possible directed acyclic graphs, DAGs, each weighted by its posterior probability. For approximate averaging, the most popular method has been Markov chain Monte Carlo, MCMC. It was recently shown that collapsing the sampling space from DAGs to suitably defined ordered partitions of the nodes substantially expedites the chain's convergence; this partition-MCMC is similar to order-MCMC on node orderings, but it avoids biasing the sampling distribution. Here, we further collapse the state space by merging some number of adjacent members of a partition into layers. This renders the computation of the (unnormalized) posterior probability of a state, called layering, more involved, for which task we give an efficient dynamic programming algorithm. Our empirical studies suggest that the resulting layering-MCMC is superior to partition-MCMC in terms of mixing time and estimation accuracy.Peer reviewe