Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon

The interest in using information to improve the quality of living in large urban areas and its governance efficiency has been around for decades. Nevertheless, the improvements in Information and Communications Technology has sparked a new dynamic in academic research, usually under the umbrella term of Smart Cities. This concept of Smart City can probably be translated, in a simplified version, into cities that are lived, managed and developed in an information-saturated environment. While it makes perfect sense and we can easily foresee the benefits of such a concept, presently there are still several significant challenges that need to be tackled before we can materialize this vision. In this work we aim at providing a small contribution in this direction, which maximizes the relevancy of the available information resources. One of the most detailed and geographically relevant information resource available, for the study of cities, is the census, more specifically the data available at block level (Subsecção Estatística). In this work, we use Self-Organizing Maps (SOM) and the variant Geo-SOM to explore the block level data from the Portuguese census of Lisbon city, for the years of 2001 and 2011. We focus on gauging change, proposing ways that allow the comparison of the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at producing a two-fold portrait: one, of the evolution of Lisbon during the first decade of the XXI century, another, of how the census dataset and SOM’s can be used to produce an informational framework for the study of cities.

Obtenció de jerarquies d'estats cel·lulars via web

Hi ha diversos mètodes d'anàlisi que duen a terme una agrupació global de la sèries de mostres de microarrays, com SelfOrganizing Maps, o que realitzen agrupaments locals tenint en compte només un subconjunt de gens coexpressats, com Biclustering, entre d'altres. En aquest projecte s'ha desenvolupat una aplicació web: el PCOPSamplecl, és una eina que pertany als mètodes d'agrupació (clustering) local, que no busca subconjunts de gens coexpresats (anàlisi de relacions linials), si no parelles de gens que davant canvis fenotípics, la seva relació d'expressió pateix fluctuacions. El resultats del PCOPSamplecl seràn les diferents distribucions finals de clusters i les parelles de gens involucrades en aquests canvis fenotípics. Aquestes parelles de gens podràn ser estudiades per trobar la causa i efecte del canvi fenotípic. A més, l'eina facilita l'estudi de les dependències entre les diferents distribucions de clusters que proporciona l'aplicació per poder estudiar la intersecció entre clusters o l'aparició de subclusters (2 clusters d'una mateixa agrupació de clusters poden ser subclusters d'altres clusters de diferents distribucions de clusters). L'eina és disponible al servidor: http://revolutionresearch.uab.es/

Myosin Vs organize actin cables in fission yeast.

Myosin V motors are believed to contribute to cell polarization by carrying cargoes along actin tracks. In Schizosaccharomyces pombe, Myosin Vs transport secretory vesicles along actin cables, which are dynamic actin bundles assembled by the formin For3 at cell poles. How these flexible structures are able to extend longitudinally in the cell through the dense cytoplasm is unknown. Here we show that in myosin V (myo52 myo51) null cells, actin cables are curled, bundled, and fail to extend into the cell interior. They also exhibit reduced retrograde flow, suggesting that formin-mediated actin assembly is impaired. Myo52 may contribute to actin cable organization by delivering actin regulators to cell poles, as myoV defects are partially suppressed by diverting cargoes toward cell tips onto microtubules with a kinesin 7-Myo52 tail chimera. In addition, Myo52 motor activity may pull on cables to provide the tension necessary for their extension and efficient assembly, as artificially tethering actin cables to the nuclear envelope via a Myo52 motor domain restores actin cable extension and retrograde flow in myoV mutants. Together these in vivo data reveal elements of a self-organizing system in which the motors shape their own tracks by transporting cargoes and exerting physical pulling forces.

The Tea4-PP1 landmark promotes local growth by dual Cdc42 GEF recruitment and GAP exclusion.

Cell polarization relies on small GTPases, such as Cdc42, which can break symmetry through self-organizing principles, and landmarks that define the axis of polarity. In fission yeast, microtubules deliver the Tea1-Tea4 complex to mark cell poles for growth, but how this complex activates Cdc42 is unknown. Here, we show that ectopic targeting of Tea4 to cell sides promotes the local activation of Cdc42 and cell growth. This activity requires that Tea4 binds the type I phosphatase (PP1) catalytic subunit Dis2 or Sds21, and ectopic targeting of either catalytic subunit is similarly instructive for growth. The Cdc42 guanine-nucleotide-exchange factor Gef1 and the GTPase-activating protein Rga4 are required for Tea4-PP1-dependent ectopic growth. Gef1 is recruited to ectopic Tea4 and Dis2 locations to promote Cdc42 activation. By contrast, Rga4 is locally excluded by Tea4, and its forced colocalization with Tea4 blocks ectopic growth, indicating that Rga4 must be present, but at sites distinct from Tea4. Thus, a Tea4-PP1 landmark promotes local Cdc42 activation and growth both through Cdc42 GEF recruitment and by creating a local trough in a Cdc42 GAP.

Bayesian image reconstruction with space-variant noise suppression

In this paper we present a Bayesian image reconstruction algorithm with entropy prior (FMAPE) that uses a space-variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of different statistical characteristics, thus avoiding the large residuals resulting from algorithms that use a constant hyperparameter. In the first implementation of the algorithm, we begin by segmenting a Maximum Likelihood Estimator (MLE) reconstruction. The segmentation method is based on using a wavelet decomposition and a self-organizing neural network. The result is a predetermined number of extended regions plus a small region for each star or bright object. To assign a different value of the hyperparameter to each extended region and star, we use either feasibility tests or cross-validation methods. Once the set of hyperparameters is obtained, we carried out the final Bayesian reconstruction, leading to a reconstruction with decreased bias and excellent visual characteristics. The method has been applied to data from the non-refurbished Hubble Space Telescope. The method can be also applied to ground-based images.

A dynamic model for stem cell homeostasis and patterning in Arabidopsis meristems.

Plants maintain stem cells in their meristems as a source for new undifferentiated cells throughout their life. Meristems are small groups of cells that provide the microenvironment that allows stem cells to prosper. Homeostasis of a stem cell domain within a growing meristem is achieved by signalling between stem cells and surrounding cells. We have here simulated the origin and maintenance of a defined stem cell domain at the tip of Arabidopsis shoot meristems, based on the assumption that meristems are self-organizing systems. The model comprises two coupled feedback regulated genetic systems that control stem cell behaviour. Using a minimal set of spatial parameters, the mathematical model allows to predict the generation, shape and size of the stem cell domain, and the underlying organizing centre. We use the model to explore the parameter space that allows stem cell maintenance, and to simulate the consequences of mutations, gene misexpression and cell ablations.

Community Policing in Switzerland's Major Urban Areas: An Observational Study of the Implementation and Impact Using Geospatial Data Mining

La présente étude est à la fois une évaluation du processus de la mise en oeuvre et des impacts de la police de proximité dans les cinq plus grandes zones urbaines de Suisse - Bâle, Berne, Genève, Lausanne et Zurich. La police de proximité (community policing) est à la fois une philosophie et une stratégie organisationnelle qui favorise un partenariat renouvelé entre la police et les communautés locales dans le but de résoudre les problèmes relatifs à la sécurité et à l'ordre public. L'évaluation de processus a analysé des données relatives aux réformes internes de la police qui ont été obtenues par l'intermédiaire d'entretiens semi-structurés avec des administrateurs clés des cinq départements de police, ainsi que dans des documents écrits de la police et d'autres sources publiques. L'évaluation des impacts, quant à elle, s'est basée sur des variables contextuelles telles que des statistiques policières et des données de recensement, ainsi que sur des indicateurs d'impacts construit à partir des données du Swiss Crime Survey (SCS) relatives au sentiment d'insécurité, à la perception du désordre public et à la satisfaction de la population à l'égard de la police. Le SCS est un sondage régulier qui a permis d'interroger des habitants des cinq grandes zones urbaines à plusieurs reprises depuis le milieu des années 1980. L'évaluation de processus a abouti à un « Calendrier des activités » visant à créer des données de panel permettant de mesurer les progrès réalisés dans la mise en oeuvre de la police de proximité à l'aide d'une grille d'évaluation à six dimensions à des intervalles de cinq ans entre 1990 et 2010. L'évaluation des impacts, effectuée ex post facto, a utilisé un concept de recherche non-expérimental (observational design) dans le but d'analyser les impacts de différents modèles de police de proximité dans des zones comparables à travers les cinq villes étudiées. Les quartiers urbains, délimités par zone de code postal, ont ainsi été regroupés par l'intermédiaire d'une typologie réalisée à l'aide d'algorithmes d'apprentissage automatique (machine learning). Des algorithmes supervisés et non supervisés ont été utilisés sur les données à haute dimensionnalité relatives à la criminalité, à la structure socio-économique et démographique et au cadre bâti dans le but de regrouper les quartiers urbains les plus similaires dans des clusters. D'abord, les cartes auto-organisatrices (self-organizing maps) ont été utilisées dans le but de réduire la variance intra-cluster des variables contextuelles et de maximiser simultanément la variance inter-cluster des réponses au sondage. Ensuite, l'algorithme des forêts d'arbres décisionnels (random forests) a permis à la fois d'évaluer la pertinence de la typologie de quartier élaborée et de sélectionner les variables contextuelles clés afin de construire un modèle parcimonieux faisant un minimum d'erreurs de classification. Enfin, pour l'analyse des impacts, la méthode des appariements des coefficients de propension (propensity score matching) a été utilisée pour équilibrer les échantillons prétest-posttest en termes d'âge, de sexe et de niveau d'éducation des répondants au sein de chaque type de quartier ainsi identifié dans chacune des villes, avant d'effectuer un test statistique de la différence observée dans les indicateurs d'impacts. De plus, tous les résultats statistiquement significatifs ont été soumis à une analyse de sensibilité (sensitivity analysis) afin d'évaluer leur robustesse face à un biais potentiel dû à des covariables non observées. L'étude relève qu'au cours des quinze dernières années, les cinq services de police ont entamé des réformes majeures de leur organisation ainsi que de leurs stratégies opérationnelles et qu'ils ont noué des partenariats stratégiques afin de mettre en oeuvre la police de proximité. La typologie de quartier développée a abouti à une réduction de la variance intra-cluster des variables contextuelles et permet d'expliquer une partie significative de la variance inter-cluster des indicateurs d'impacts avant la mise en oeuvre du traitement. Ceci semble suggérer que les méthodes de géocomputation aident à équilibrer les covariables observées et donc à réduire les menaces relatives à la validité interne d'un concept de recherche non-expérimental. Enfin, l'analyse des impacts a révélé que le sentiment d'insécurité a diminué de manière significative pendant la période 2000-2005 dans les quartiers se trouvant à l'intérieur et autour des centres-villes de Berne et de Zurich. Ces améliorations sont assez robustes face à des biais dus à des covariables inobservées et covarient dans le temps et l'espace avec la mise en oeuvre de la police de proximité. L'hypothèse alternative envisageant que les diminutions observées dans le sentiment d'insécurité soient, partiellement, un résultat des interventions policières de proximité semble donc être aussi plausible que l'hypothèse nulle considérant l'absence absolue d'effet. Ceci, même si le concept de recherche non-expérimental mis en oeuvre ne peut pas complètement exclure la sélection et la régression à la moyenne comme explications alternatives. The current research project is both a process and impact evaluation of community policing in Switzerland's five major urban areas - Basel, Bern, Geneva, Lausanne, and Zurich. Community policing is both a philosophy and an organizational strategy that promotes a renewed partnership between the police and the community to solve problems of crime and disorder. The process evaluation data on police internal reforms were obtained through semi-structured interviews with key administrators from the five police departments as well as from police internal documents and additional public sources. The impact evaluation uses official crime records and census statistics as contextual variables as well as Swiss Crime Survey (SCS) data on fear of crime, perceptions of disorder, and public attitudes towards the police as outcome measures. The SCS is a standing survey instrument that has polled residents of the five urban areas repeatedly since the mid-1980s. The process evaluation produced a "Calendar of Action" to create panel data to measure community policing implementation progress over six evaluative dimensions in intervals of five years between 1990 and 2010. The impact evaluation, carried out ex post facto, uses an observational design that analyzes the impact of the different community policing models between matched comparison areas across the five cities. Using ZIP code districts as proxies for urban neighborhoods, geospatial data mining algorithms serve to develop a neighborhood typology in order to match the comparison areas. To this end, both unsupervised and supervised algorithms are used to analyze high-dimensional data on crime, the socio-economic and demographic structure, and the built environment in order to classify urban neighborhoods into clusters of similar type. In a first step, self-organizing maps serve as tools to develop a clustering algorithm that reduces the within-cluster variance in the contextual variables and simultaneously maximizes the between-cluster variance in survey responses. The random forests algorithm then serves to assess the appropriateness of the resulting neighborhood typology and to select the key contextual variables in order to build a parsimonious model that makes a minimum of classification errors. Finally, for the impact analysis, propensity score matching methods are used to match the survey respondents of the pretest and posttest samples on age, gender, and their level of education for each neighborhood type identified within each city, before conducting a statistical test of the observed difference in the outcome measures. Moreover, all significant results were subjected to a sensitivity analysis to assess the robustness of these findings in the face of potential bias due to some unobserved covariates. The study finds that over the last fifteen years, all five police departments have undertaken major reforms of their internal organization and operating strategies and forged strategic partnerships in order to implement community policing. The resulting neighborhood typology reduced the within-cluster variance of the contextual variables and accounted for a significant share of the between-cluster variance in the outcome measures prior to treatment, suggesting that geocomputational methods help to balance the observed covariates and hence to reduce threats to the internal validity of an observational design. Finally, the impact analysis revealed that fear of crime dropped significantly over the 2000-2005 period in the neighborhoods in and around the urban centers of Bern and Zurich. These improvements are fairly robust in the face of bias due to some unobserved covariate and covary temporally and spatially with the implementation of community policing. The alternative hypothesis that the observed reductions in fear of crime were at least in part a result of community policing interventions thus appears at least as plausible as the null hypothesis of absolutely no effect, even if the observational design cannot completely rule out selection and regression to the mean as alternative explanations.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

Waste management policies for industrial symbiosis development: case studies in European countries

Industrial symbiosis (IS) emerged as a self-organizing business strategy among firms that are willing to cooperate to improve their economic and environmental performance. The adoption of such cooperative strategies relates to increasing costs of waste management, most of which are driven by policy and legislative requirements. Development of IS depends on an enabling context of social, informational, technological, economical and political factors. The power to influence this context varies among the agents involved such as the government, businesses or coordinating entities. Governmental intervention, as manifested through policies, could influence a wider range of factors; and we believe this is an area which is under-researched. This paper aims to critically appraise the waste policy interventions from supra-national to sub-national levels of government. A case study methodology has been applied to four European countries i.e. Denmark, the UK, Portugal and Switzerland, in which IS emerged or is being fostered. The findings suggest that there are commonalities in policy instruments that may have led to an IS enabling context. The paper concludes with lessons learnt and recommendations on shaping the policy context for IS development.

Yhteiskunnallisen sisältöpalvelun sosioteknologinen tarkastelu Kohosen itseorganisoituvien karttojen avulla - Case deittipalvelu

Työn tavoitteena on tutkia deittipalvelun käyttäjien anonyymiaineistoa neuroverkko-opetuksessa segmentoituneiden piirrekarttojen (SOM, Self-Organizing Map) avulla. Näiden piirrekarttojen avulla on tarkoitus selvittää, löytyykö mahdollisesti selkeitä SMS- ja e-mail - käyttäjäryhmiä. Tutkimusta lähestytään perehtymällä ensin yrityksen tekniseen palvelualusta-arkkitehtuuriin ja myös varsinaiseen deittipalveluun käyttäjän kannalta.Tutkimus aloitettiin koodaamalla tietoaineisto SOM Toolbox-ohjelmalle käytettäväksi. Varsinaisia tutkimustuloksia analysoitiin valitsemalla otoksia neuroverkko-opetuksessa segmentoituneista piirrekartoista. Saadut tulokset osoittavat, ettäSOM-teknologia soveltuu hyvin sisältöpalveluiden sosioteknologiseen tutkimukseen ja sitä on myös mahdollista käyttää asiakkuudenhallinnassa erilaisten käyttäjäryhmien profilointiin.

Keraamisten laattojen luokittelu väripiirteiden avulla

Vaikka keraamisten laattojen valmistusprosessi onkin täysin automatisoitu, viimeinen vaihe eli laaduntarkistus ja luokittelu tehdään yleensä ihmisvoimin. Automaattinen laaduntarkastus laattojen valmistuksessa voidaan perustella taloudellisuus- ja turvallisuusnäkökohtien avulla. Tämän työn tarkoituksena on kuvata tutkimusprojektia keraamisten laattojen luokittelusta erilaisten väripiirteiden avulla. Oleellisena osana tutkittiin RGB- ja spektrikuvien välistä eroa. Työn teoreettinen osuus käy läpi aiemmin aiheesta tehdyn tutkimuksen sekä antaa taustatietoa konenäöstä, hahmontunnistuksesta, luokittelijoista sekä väriteoriasta. Käytännön osan aineistona oli 25 keraamista laattaa, jotka olivat viidestä eri luokasta. Luokittelussa käytettiin apuna k:n lähimmän naapurin (k-NN) luokittelijaa sekä itseorganisoituvaa karttaa (SOM). Saatuja tuloksia verrattiin myös ihmisten tekemään luokitteluun. Neuraalilaskenta huomattiin tärkeäksi työkaluksi spektrianalyysissä. SOM:n ja spektraalisten piirteiden avulla saadut tulokset olivat lupaavia ja ainoastaan kromatisoidut RGB-piirteet olivat luokittelussa parempia kuin nämä.

Spektrikuvien tiivistäminen itseorganisoituvan kartan avulla.

Vuosi vuodelta kasvava tietokoneiden prosessointikyky on mahdollistanut harmaataso- ja RGB-värikuvia tarkempien spektrikuvien käsittelyn järjellisessä ajassa ilman suuria kustannuksia. Ongelmana on kuitenkin, ettei talletus- ja tiedonsiirtomedia ole kehittynyt prosessointikyvyn vauhdissa. Ratkaisu tähän ongelmaan on spektrikuvien tiivistäminen talletuksen ja tiedonsiirron ajaksi. Tässä työssä esitellään menetelmä, jossa spektrikuva tiivistetään kahdessa vaiheessa: ensin ryhmittelemällä itseorganisoituvan kartan (SOM) avulla ja toisessa vaiheessa jatketaan tiivistämistä perinteisin menetelmin. Saadut tiivistyssuhteet ovat merkittäviä vääristymän pysyessä siedettävänä. Työ on tehty Lappeenrannan teknillisen korkeakoulun Tietotekniikan osaston Tietojenkäsittelytekniikan tutkimuslaboratoriossa osana laajempaa kuvantiivistyksen tutkimushanketta.

Multidimensional Deprivation in Contemporary Switzerland Across Social Groups and Time

We have investigated the phenomenon of deprivation in contemporary Switzerland through the adoption of a multidimensional, dynamic approach. By applying Self Organizing Maps (SOM) to a set of 33 non-monetary indicators from the 2009 wave of the Swiss Household Panel (SHP), we identified 13 prototypical forms (or clusters) of well-being, financial vulnerability, psycho-physiological fragility and deprivation within a topological dimensional space. Then new data from the previous waves (2003 to 2008) were classified by the SOM model, making it possible to estimate the weight of the different clusters in time and reconstruct the dynamics of stability and mobility of individuals within the map. Looking at the transition probabilities between year t and year t+1, we observed that the paths of mobility which catalyze the largest number of observations are those connecting clusters that are adjacent on the topological space.

Asiakkuudenhallinnan tukeminen neuroverkkoteknologian avulla

CRM on yritysten tietojärjestelmä, jolla voidaan tukea asiakkuuden hallintaa ja kehittämistä. Monilla suuryrityksillä on paljon asiakkaita ja niiden on mahdotonta tunnistaa asiakkaitaan yksilöinä. Kuitenkin asiakkaat arvostavat yhä enemmän henkilökohtaista palvelua ja kontakteja. Yritysten asiakastietokantoihin kertyy runsaasti tietoa asiakkaista ja heidän ostokäyttäytymisestään. Suuresta informaatiomäärästä johtuen tarvitaan kehittynyttä tietotekniikkaa asiakkaiden tyypittelyyn ja asiakastarpeiden tunnistamiseen. Tässä diplomityössä kehitetään neuroCRM-teoriaa määrällisesti suuren ja monimutkaisen asiakasinformaation hallintaan. Teoria perustuu itseorganisoituvien neuroverkkojen käyttöön asiakasinformaation analysoimiseksi CRM-järjestelmässä. Asiakkaat segmentoidaan ja personoidaan iteratiivisia SOM-analyysejä suorittamalla. Tulosten perusteella kehitetään asiakkaiden yksilöllisyyttä huomioivia markkinointikeinoja ja käytetään uusia kanavia, esimerkiksi mobiilia viestintätekniikkaa asiakkaiden tavoittamiseen. Asiakaskannattavuuden parantamiseksi voi- daan tehdä strategisia valintoja ja päätöksiä markkinoinnin kohdentamista varten.

Unsupervised visual object categorization

The number of digital images has been increasing exponentially in the last few years. People have problems managing their image collections and finding a specific image. An automatic image categorization system could help them to manage images and find specific images. In this thesis, an unsupervised visual object categorization system was implemented to categorize a set of unknown images. The system is unsupervised, and hence, it does not need known images to train the system which needs to be manually obtained. Therefore, the number of possible categories and images can be huge. The system implemented in the thesis extracts local features from the images. These local features are used to build a codebook. The local features and the codebook are then used to generate a feature vector for an image. Images are categorized based on the feature vectors. The system is able to categorize any given set of images based on the visual appearance of the images. Images that have similar image regions are grouped together in the same category. Thus, for example, images which contain cars are assigned to the same cluster. The unsupervised visual object categorization system can be used in many situations, e.g., in an Internet search engine. The system can categorize images for a user, and the user can then easily find a specific type of image.