An Contemplated Approach for Criminality Data using Mining Algorithm

We propose an approach for the arrangement and execution of bad behavior area and criminal recognizing confirmation for Indian urban groups using data mining frameworks. Our approach is parceled into six modules, to be particular�information extraction (DE), information preprocessing (DP), grouping, Google outline, characterization and WEKA� execution. To begin with module, DE expels the unstructured wrongdoing dataset from various wrongdoing Web sources, in the midst of the season of 2000� 2018. Second module, DP cleans, facilitates and diminishes the removed wrongdoing data into sorted out 5,038 wrongdoing events. We address these events using 35 predefined wrongdoing attributes. Secure measures are taken for the wrongdoing database accessibility. Rest four modules are useful for bad behavior acknowledgment, criminal recognizing evidence and desire, and bad behavior affirmation, independently. Wrongdoing acknowledgment is explored using k-suggests gathering, which iteratively makes two wrongdoing bundles that rely upon equivalent wrongdoing properties. Google portray observation to k-infers. Criminal conspicuous verification and estimate is dismembered using KNN portrayal. Bad behavior check of our results is done using WEKA�. WEKA� checks an exactness of 93.62 and 93.99 % in the course of action of two bad behavior clusters using picked bad behavior attributes. Our approach contributes in the change of the overall population by helping the looking at workplaces in bad behavior area and guilty parties' recognizing confirmation, and in this way decreasing the bad behavior rates. Wrongdoings are a social unsettling influence and cost the overall population to an awesome degree from various perspectives. Any examination that can help in separating and comprehending wrongdoing speedier pays for itself. Crime data mining has the capacity of extricating helpful data and concealed examples from the substantial wrongdoing informational indexes. The crime data mining challenges are getting to be fortifying open doors for the coming years. Since the writing of crime information mining has expanded energetically as of late, it winds up obligatory to build up a diagram of the cutting edge. This orderly survey centers around crime data mining procedures and innovations utilized as a part of past investigations. The current work is grouped into various classifications and is introduced utilizing perceptions. This paper additionally demonstrates a few difficulties identified with crime data research

Ubiquitous intelligence for smart cities: a public safety approach

Citizen-centered safety enhancement is an integral component of public safety and a top priority for decision makers in a smart city development. However, public safety agencies are constantly faced with the challenge of deterring crime. While most smart city initiatives have placed emphasis on the use of modern technology for fighting crime, this may not be sufficient to achieve a sustainable safe and smart city in a resource constrained environment, such as in Africa. In particular, crime series which is a set of crimes considered to have been committed by the same offender is currently less explored in developing nations and has great potential in helping to fight against crime and promoting safety in smart cities. This research focuses on detecting the situation of crime through data mining approaches that can be used to promote citizens' safety, and assist security agencies in knowledge-driven decision support, such as crime series identification. While much research has been conducted on crime hotspots, not enough has been done in the area of identifying crime series. This thesis presents a novel crime clustering model, CriClust, for crime series pattern (CSP) detection and mapping to derive useful knowledge from a crime dataset, drawing on sound scientific and mathematical principles, as well as assumptions from theories of environmental criminology. The analysis is augmented using a dual-threshold model, and pattern prevalence information is encoded in similarity graphs. Clusters are identified by finding highly-connected subgraphs using adaptive graph size and Monte-Carlo heuristics in the Karger-Stein mincut algorithm. We introduce two new interest measures: (i) Proportion Difference Evaluation (PDE), which reveals the propagation effect of a series and dominant series; and (ii) Pattern Space Enumeration (PSE), which reveals underlying strong correlations and defining features for a series. Our findings on experimental quasi-real data set, generated based on expert knowledge recommendation, reveal that identifying CSP and statistically interpretable patterns could contribute significantly to strengthening public safety service delivery in a smart city development. Evaluation was conducted to investigate: (i) the reliability of the model in identifying all inherent series in a crime dataset; (ii) the scalability of the model with varying crime records volume; and (iii) unique features of the model compared to competing baseline algorithms and related research. It was found that Monte Carlo technique and adaptive graph size mechanism for crime similarity clustering yield substantial improvement. The study also found that proportion estimation (PDE) and PSE of series clusters can provide valuable insight into crime deterrence strategies. Furthermore, visual enhancement of clusters using graphical approaches to organising information and presenting a unified viable view promotes a prompt identification of important areas demanding attention. Our model particularly attempts to preserve desirable and robust statistical properties. This research presents considerable empirical evidence that the proposed crime cluster (CriClust) model is promising and can assist in deriving useful crime pattern knowledge, contributing knowledge services for public safety authorities and intelligence gathering organisations in developing nations, thereby promoting a sustainable "safe and smart" city

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe size and dimensionality of available geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GIS-minded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.(...

Ventilation in occupied homes: measurement, performance and sociotechnical perspectives

In the UK, steps have been taken to reduce air permeability of buildings and reduce their energy consumption due to unplanned ventilation. However, adequate ventilation is required for good indoor air quality. The building regulations require means for adequate ventilation in new buildings for good indoor air quality, and in England Approved Document F (ADF) sets out how this may be achieved. Nonetheless, few detailed studies of ventilation in occupied homes have been carried out. This project addresses aspects of ventilation measurement, performance of ventilation systems and the sociotechnical nature of ventilation in occupied homes. Ventilation in occupied buildings is driven by building characteristics, ventilation equipment, weather conditions and occupant actions and therefore can be highly variable. Despite this, much ventilation research in occupied homes either measures a long-term average ventilation rate or collects a small number of `snap-shot’ measurements of ventilation rate. This research developed a method for measuring ventilation rates in occupied homes based on the tracer gas decay technique using metabolic CO₂. The method was applied in four occupied dwellings over 6 months to give more than 500 ventilation rate measurements. These results facilitated assessment of the performance of the ventilation system and exploration of the variation in ventilation rates. This revealed significant differences in the ventilation rates experienced by occupants in the different dwellings and highlighted shortcomings in the planned ventilation system. Ventilation in occupied homes is strongly influenced by occupants. The final part of the research used a social practice theory framework to compare the participants’ practices with the intended uses of ventilation equipment implicit in ADF. This revealed that although the participants shared many of ADF’s goals in terms of the air in their homes, their practices were more nuanced than ADF and that their use of the ventilation equipment did not reflect ADF’s intentions

Texas Register

A weekly publication, the Texas Register serves as the journal of state agency rulemaking for Texas. Information published in the Texas Register includes proposed, adopted, withdrawn and emergency rule actions, notices of state agency review of agency rules, governor's appointments, attorney general opinions, and miscellaneous documents such as requests for proposals. After adoption, these rulemaking actions are codified into the Texas Administrative Code