The Experts below are selected from a list of 3609 Experts worldwide ranked by ideXlab platform
Donald E Brown - One of the best experts on this subject based on the ideXlab platform.
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The spatio-temporal modeling for Criminal Incidents
Security Informatics, 2012Co-Authors: Xiaofeng Wang, Donald E BrownAbstract:Law enforcement agencies monitor Criminal Incidents. With additional geographic and demographic data, law enforcement analysts look for spatio-temporal patterns in these Incidents in order to predict future Criminal activity. When done correctly these predictions can inform actions that can improve security and reduce the impact of crime. Effective prediction requires the development of models that can find and incorporate the important associative and causative variables available in the data. This paper describes a new approach that uses spatio-temporal generalized additive models (ST-GAMs) to discover underlying factors related to crimes and predict future Incidents. In addition, the paper shows extensions of the ST-GAM approach to produce local spatio-temporal generalized additive models (LST-GAMs). These local models can better predict Criminal Incidents conditioned on regions. Both models can fully utilize a variety of data types, such as spatial, temporal, geographic, and demographic data, to make predictions. We describe how to estimate the parameters for ST-GAM using iteratively re-weighted least squares and maximum likelihood and show that the resulting estimates provide for model interpretability. This paper also discusses methods to generate regions for LST-GAM. Lastly the paper discusses the evaluation of LST-GAM and ST-GAM with actual Criminal Incident data from Charlottesville, Virginia. The evaluation results show that both models from this new approach outperform previous spatial models in predicting future Criminal Incidents.
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SBP - Automatic crime prediction using events extracted from twitter posts
Social Computing Behavioral - Cultural Modeling and Prediction, 2012Co-Authors: Xiaofeng Wang, Matthew S. Gerber, Donald E BrownAbstract:Prior work on Criminal Incident prediction has relied primarily on the historical crime record and various geospatial and demographic information sources. Although promising, these models do not take into account the rich and rapidly expanding social media context that surrounds Incidents of interest. This paper presents a preliminary investigation of Twitter-based Criminal Incident prediction. Our approach is based on the automatic semantic analysis and understanding of natural language Twitter posts, combined with dimensionality reduction via latent Dirichlet allocation and prediction via linear modeling. We tested our model on the task of predicting future hit-and-run crimes. Evaluation results indicate that the model comfortably outperforms a baseline model that predicts hit-and-run Incidents uniformly across all days.
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ISI - The spatio-temporal generalized additive model for Criminal Incidents
Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, 2011Co-Authors: Xiaofeng Wang, Donald E BrownAbstract:Law enforcement agencies need to model spatio-temporal patterns of Criminal Incidents. With well developed models, they can study the causality of crimes and predict future Criminal Incidents, and they can use the results to help prevent crimes. In this paper, we described our newly developed spatio-temporal generalized additive model (S-T GAM) to discover underlying factors related to crimes and predict future Incidents. The model can fully utilize many different types of data, such as spatial, temporal, geographic, and demographic data, to make predictions. We efficiently estimated the parameters for S-T GAM using iteratively re-weighted least squares and maximum likelihood and the resulting estimates provided for model interpretability. In this paper we showed the evaluation of S-T GAM with the actual Criminal Incident data from Charlottesville, Virginia. The evaluation results showed that S-T GAM outperformed the previous spatial prediction models in predicting future Criminal Incidents.
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An outlier-based data association method for linking Criminal Incidents
Decision Support Systems, 2006Co-Authors: Song Lin, Donald E BrownAbstract:Serial Criminals are a major threat in the modern society. Associating Incidents committed by the same offender is of great importance in studying serial Criminals. In this paper, we present a new outlier-based approach to resolve this Criminal Incident association problem. In this approach, Criminal Incident data are first modeled into a number of cells, and then a measurement function, called outlier score function, is defined over these cells. Incidents in a cell are determined to be associated with each other when the score is significant enough. We applied our approach to a robbery dataset from Richmond, VA. Results show that this method can effectively solve the Criminal Incident association problem.
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Criminal Incident prediction using a point pattern based density model
International Journal of Forecasting, 2003Co-Authors: Donald E BrownAbstract:Abstract Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week. Current practice simply assumes that spatial clusters of crimes or “hot spots” observed in the previous week will persist to the next week. This paper introduces a multivariate prediction model for hot spots that relates the features in an area to the predicted occurrence of crimes through the preference structure of Criminals. We use a point-pattern-based transition density model for space–time event prediction that relies on Criminal preference discovery as observed in the features chosen for past crimes. The resultant model outperforms the current practices, as demonstrated statistically by an application to breaking and entering Incidents in Richmond, VA.
Arief Wisnu Wardhana - One of the best experts on this subject based on the ideXlab platform.
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PENYIDIKAN PERKARA TINDAK PIDANA PENGOPLOSAN BERAS BULOG DI KABUPATEN LAHAT OLEH DIREKTORAT RESERSE KRIMINAL KHUSUS POLDA SUMATERA SELATAN
Program Studi Magister Ilmu Hukum Universitas Kader Bangsa, 2019Co-Authors: Arief Wisnu WardhanaAbstract:The investigation conducted by the Special Crime Directorate, South Sumatra Regional Police conducted on Tuesday July 18, 2017, related to the reproses of rice is not good quality and contrary to Article 1 paragraph 4 of Law No. 18 of 2012 which basically rice must be good quality. Good quality in terms of decreased quality of rice (yellow, dusty, dull, lice, smelly). The objective of this research is to know the investigation of Bulog Rice. This study uses normative research research methods, namely legal research that focuses on the analysis of legislation. The results of the investigation of the Criminal Investigation of the Coppers occurred on Subdivre Lahat has fulfilled the requirements as required in Article 184 paragraph (1) of the Criminal Procedure, both the requirements of witness testimony and expert information on the quality down rice have met the requirements. so the investigation does not find evidence of Criminal Incidents as prescribed in Article 62 paragraph (1) in conjunction with Article 8 paragraph (1) a, (2) and (3) of Law no. It is not proven because the reprocessing rice has not been distributed, therefore the investigator issues the SP3 on the grounds that there is insufficient evidence. The investigator's of rice quality drops is basically absent, but there is a mistake in the focus of the investigation of the Criminal Incident according to Article 62 paragraph (1) jo Article 8 paragraph (1) a, (2) and (3) of Law no. 8 Year 1999.Penyelidikan dan penyidikan yang dilakukan oleh Direktorat Kriminal Khusus, Polda Sumatera Selatan dilakukan pada pada hari Selasa tanggal 18 Juli 2017, terkait dengan reproses beras tidak berkualitas baik dan bertentangan dengan Pasal 1 angka 4 Undang-Undang No . 18 Tahun 2012 yang pada dasarnya beras harus bermutu baik. Mutu baik dalam arti penurunan kualitas beras (berwarna kuning, berdebu, kusam, kutu, berbau). Penelitian bertujuan untuk mengetahui penyidikan tindak pidana pengoplosan Beras Bulog. Penelitian ini menggunakan metode penelitian hukum normatif, yaitu penelitian hukum yang menitikberatkan pada analisis kasus dan peraturan perundang-undangan. Hasil penelitian Penyidikan Tindak Pidana Pengoplosan terjadi pada Subdivre Lahat telah memenuhi persyaratan yang telah dipersyaratkan pada Pasal 184 ayat (1) KUHAP baik itu persyaratan keterangan saksi maupun keterangan ahli terhadap beras turun mutu.Penyidik menerbitkan SP3 dengan alasan tidak diperoleh bukti cukup.Kendala penyidik dalam penyidikan terdapat kekeliruan dalam fokus penyidikan terhadap terjadinya peristiwa pidana menurut Pasal 62 ayat (1) jo Pasal 8 ayat (1) huruf a, (2) dan (3) Undang-Undang No. 8 Tahun 1999
Irwanto Arief Wisnu Wardhana - One of the best experts on this subject based on the ideXlab platform.
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PENYIDIKAN PERKARA TINDAK PIDANA PENGOPLOSAN BERAS BULOG DI KABUPATEN LAHAT OLEH DIREKTORAT RESERSE KRIMINAL KHUSUS POLDA SUMATERA SELATAN
2019Co-Authors: Irwanto Arief Wisnu WardhanaAbstract:The investigation conducted by the Special Crime Directorate, South Sumatra Regional Police conducted on Tuesday July 18, 2017, related to the reproses of rice is not good quality and contrary to Article 1 paragraph 4 of Law No. 18 of 2012 which basically rice must be good quality. Good quality in terms of decreased quality of rice (yellow, dusty, dull, lice, smelly). The objective of this research is to know the investigation of Bulog Rice. This study uses normative research research methods, namely legal research that focuses on the analysis of legislation. The results of the investigation of the Criminal Investigation of the Coppers occurred on Subdivre Lahat has fulfilled the requirements as required in Article 184 paragraph (1) of the Criminal Procedure, both the requirements of witness testimony and expert information on the quality down rice have met the requirements. so the investigation does not find evidence of Criminal Incidents as prescribed in Article 62 paragraph (1) in conjunction with Article 8 paragraph (1) a, (2) and (3) of Law no. It is not proven because the reprocessing rice has not been distributed, therefore the investigator issues the SP3 on the grounds that there is insufficient evidence. The investigator's of rice quality drops is basically absent, but there is a mistake in the focus of the investigation of the Criminal Incident according to Article 62 paragraph (1) jo Article 8 paragraph (1) a, (2) and (3) of Law no. 8 Year 1999.
Xiaofeng Wang - One of the best experts on this subject based on the ideXlab platform.
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The spatio-temporal modeling for Criminal Incidents
Security Informatics, 2012Co-Authors: Xiaofeng Wang, Donald E BrownAbstract:Law enforcement agencies monitor Criminal Incidents. With additional geographic and demographic data, law enforcement analysts look for spatio-temporal patterns in these Incidents in order to predict future Criminal activity. When done correctly these predictions can inform actions that can improve security and reduce the impact of crime. Effective prediction requires the development of models that can find and incorporate the important associative and causative variables available in the data. This paper describes a new approach that uses spatio-temporal generalized additive models (ST-GAMs) to discover underlying factors related to crimes and predict future Incidents. In addition, the paper shows extensions of the ST-GAM approach to produce local spatio-temporal generalized additive models (LST-GAMs). These local models can better predict Criminal Incidents conditioned on regions. Both models can fully utilize a variety of data types, such as spatial, temporal, geographic, and demographic data, to make predictions. We describe how to estimate the parameters for ST-GAM using iteratively re-weighted least squares and maximum likelihood and show that the resulting estimates provide for model interpretability. This paper also discusses methods to generate regions for LST-GAM. Lastly the paper discusses the evaluation of LST-GAM and ST-GAM with actual Criminal Incident data from Charlottesville, Virginia. The evaluation results show that both models from this new approach outperform previous spatial models in predicting future Criminal Incidents.
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SBP - Automatic crime prediction using events extracted from twitter posts
Social Computing Behavioral - Cultural Modeling and Prediction, 2012Co-Authors: Xiaofeng Wang, Matthew S. Gerber, Donald E BrownAbstract:Prior work on Criminal Incident prediction has relied primarily on the historical crime record and various geospatial and demographic information sources. Although promising, these models do not take into account the rich and rapidly expanding social media context that surrounds Incidents of interest. This paper presents a preliminary investigation of Twitter-based Criminal Incident prediction. Our approach is based on the automatic semantic analysis and understanding of natural language Twitter posts, combined with dimensionality reduction via latent Dirichlet allocation and prediction via linear modeling. We tested our model on the task of predicting future hit-and-run crimes. Evaluation results indicate that the model comfortably outperforms a baseline model that predicts hit-and-run Incidents uniformly across all days.
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ISI - The spatio-temporal generalized additive model for Criminal Incidents
Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, 2011Co-Authors: Xiaofeng Wang, Donald E BrownAbstract:Law enforcement agencies need to model spatio-temporal patterns of Criminal Incidents. With well developed models, they can study the causality of crimes and predict future Criminal Incidents, and they can use the results to help prevent crimes. In this paper, we described our newly developed spatio-temporal generalized additive model (S-T GAM) to discover underlying factors related to crimes and predict future Incidents. The model can fully utilize many different types of data, such as spatial, temporal, geographic, and demographic data, to make predictions. We efficiently estimated the parameters for S-T GAM using iteratively re-weighted least squares and maximum likelihood and the resulting estimates provided for model interpretability. In this paper we showed the evaluation of S-T GAM with the actual Criminal Incident data from Charlottesville, Virginia. The evaluation results showed that S-T GAM outperformed the previous spatial prediction models in predicting future Criminal Incidents.
E Ravi - One of the best experts on this subject based on the ideXlab platform.
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Predicting Digital Geotechnical Forensic Investigation using Internet of Things (IoT)
Maple Tree Journals, 2019Co-Authors: D Sivakumar, E RaviAbstract:Digital Forensic Investigations is explained as are sponse to an event that has already occurred in relation to information which is highly classified or is of prime importance to a Criminal Incident. Forensics Challenges in IoT Environments IoT would soon pervade all aspects of our life from managing our home temperature to thinking cars and smart management of the cities. The application of professional engineering principles and methodologies to investigating failures and Incidents, usually to determine causation. Normally, it involves preparing a report of findings, which may form the basis for testimony in legal proceedings as an expert witness. A forensic engineer may serve as an engineering consultant to members of the legal profession and as an expert witness in courts of law, arbitration proceedings and administrative adjudication proceedings. Forensic engineering is a part of professional engineering practice that may cover all disciplines of engineering. It is a specialized set of skills that can include multidisciplinary training in failure analysis, simulation, safety, accelerated life testing and statistical analysis, as well as knowledge of the specific engineering field
