Sex Offender Treatment Program: Preliminary Description

This report provides a summary of the history of sex offender treatment in Alaska, including the current status of treatment programs offered by the Alaska Department of Corrections, a review of literature on sex offender treatment and recidivism issues, and a summary of the descriptive characteristics of individuals who came in contact with the Hiland Mountain Correctional Center from January 1987 to March 1993.Alaska Department of CorrectionsIntroduction / Sex Offender Treatment in Alaska / Literature Review / Methodology / Results / Conclusion and Recommendations / Bibliograph

Statistical Semantic Classification of Crisis Information

The rise of social media as an information channel during crisis has become key to community response. However, existing crisis awareness applications, often struggle to identify relevant information among the high volume of data that is generated over social platforms. A wide range of statistical features and machine learning methods have been researched in recent years to automatically classify this information. In this paper we aim to complement previous studies by exploring the use of semantics as additional features to identify relevant crisis in- formation. Our assumption is that entities and concepts tend to have a more consistent correlation with relevant and irrelevant information, and therefore can enhance the discrimination power of classifiers. Our results, so far, show that some classification improvements can be obtained when using semantic features, reaching +2.51% when the classifier is applied to a new crisis event (i.e., not in training set)

Justice Data Base Directory

The Justice Data Base Directory was originally published in 1988 with an introduction, 8 chapters describing Alaska justice agencies and their data holdings, and an index. It was published in looseleaf notebook format for easy updating. Four updates were published in 1989–1992, each update consisting of additional chapters, revised table of contents and index, and updates to existing pages to reflect changes such as agency addresses. Five chapters were added in 1989; five in 1990; four in 1991; and five in 1992, for a total of 27 agencies covered by the Justice Data Base Directory in its final form. For archival purposes, this record includes all five versions of the directory. The 1992 edition is the most complete.The Justice Data Base Directory, first published in 1988 with new chapters added annually through 1992, presents information about the primary databases maintained by Alaska justice agencies and the procedures to be followed for access to the data. Its availability should substantially reduce the work required to identify the sources of data for research and policy development in law, law enforcement, courts, and corrections. The 1992 update to the directory adds five chapters, for a total of 27 Alaska agencies whose justice-related data holdings are described: Alaska Court System; Alaska Judicial Council; Alaska Commission on Judicial Conduct; Alaska Department of Law; Alaska Department of Public Safety (DPS) and three agencies under DPS: Alaska Police Standards Council, Council on Domestic Violence and Sexual Assault (CDSA), and Violent Crimes Compensation Board; Alaska Department of Corrections (DOC) and Parole Board; four agencies of the Alaska Department of Health and Social Services — Bureau of Vital Statistics (Division of Public Health), Epidemiology Section (Division of Public Health), Division of Family and Youth Services, and Office of Alcoholism and Drug Abuse; Alaska Public Defender Agency; Office of Public Advocacy (OPA); Alaska Bar Association; Alaska Justice Statistical Analysis Unit; Alaska Office of Equal Employment Opportunity (Office of the Governor); Alaska Office of the Ombudsman; Alaska Legal Services Corporation; Alaska Public Offices Commission; Alaska State Commission for Human Rights; Alcoholic Beverage Control (ABC) Board; Legislative Research Agency; Legislative Affairs Agency; State Archives and Records Management Services (Alaska Department of Education). Fully indexed.Funded in part by a grant from the Bureau of Justice Statistics.1. Introduction / 2. Alaska Court System / 3. Alaska Department of Law / 4. Alaska Department of Public Safety / 5. Alaska Department of Corrections / 6. Division of Family and Youth Services, Alaska Department of Health and Social Services / 7. Alaska Bar Association / 8. Alaska Judicial Council / 9. Alaska Justice Statistical Analysis Unit / 10. Bureau of Vital Statistics, Division of Public Health, Alaska Department of Health and Social Services / 11. Alaska Office of Equal Employment Opportunity, Office of the Governor / 12. Office of Alcoholism and Drug Abuse, Alaska Department of Health and Social Services / 13. Council on Domestic Violence and Sexual Assault, Alaska Department of Public Safety / 14. Epidemiology Section, Division of Public Health, Alaska Department of Health and Social Services / 15. Violent Crimes Compensation Board, Alaska Department of Public Safety / 16. Alaska Police Standards Council, Alaska Department of Public Safety / 17. Alcoholic Beverage Control Board / 18. Alaska Office of the Ombudsman / 19. State Archives and Records Management Services, Alaska Department of Education / 20. Legislative Research Agency / 21. Legislative Affairs Agency / 22. Alaska State Commission for Human Rights / 23. Parole Board, Alaska Department of Corrections / 24. Alaska Public Offices Commission / 25. Alaska Commission on Judicial Conduct / 26. Alaska Legal Services Corporation / 27. Office of Public Advocacy / 28. Alaska Public Defender Agency / 29. Inde

Statistical models for market segmentation

It is an essential element of market research that customer preferences are considered and the heterogeneity of these preferences is recognized. By segmenting the market into homogeneous clusters the preferences of customers is addressed. Latent class methodology for conjoint analysis, proposed by Green (2000), is one of the several conjoint segmentation procedures that overcome the limitations of aggregate analysis and priori segmentation. This approach proposes the proportional odds model as a proper statistical model for ordinal categorical data in which the item attributes are included in the linear predictor. The likelihood is maximized through the EM algorithm. This paper considers two extensions of this methodology that incorporate individual characteristics into the models.peer-reviewe

Determining Quantity Model of Built-Up Occupancy Residential in Musi Banyuasin District

ABSTRACTThe determination of the quantity of dwellings in Musi Banyuasin district is a form of activity in seeing the development of the number of dwellings on built-up residential land when the existing data on the quantity of dwellings needed for the base year has not been obtained during secondary data collection. The importance of existing occupancy quantity data in planning activities, of course, is as a basis for determining the level of future occupancy shortages (projections). The purpose of this research is to determine the existing data on the number of dwellings in 2022 as an alternative form of providing the quantity of dwellings on built-up settlement land that will be used as planning data in Musi Banyuasin district. The method used is a quantitative approach with analytical methods, statistical, spatial and technical. Based on the results of the analysis, the quantity of dwelling units in Musi Banyuasin district on 7,528 hectares of built-up settlement land resulted in 205,962 dwelling units spread across all sub-districts with an average dwelling area of 148 square meters per house consisting of 124,441 households. These results are based on an assessment of the standard deviation and correlation level of 3 alternative comparison data, namely 213,645 dwelling units and 243,227 dwelling units, both of which have standard deviation and correlation values below the value of alternative 1.Keywords:  Settlement; Residential; Decision; Statistics; TechnicalABSTRAKPenentuan jumlah hunian di Kabupaten Musi Banyuasin merupakan bentuk kegiatan untuk melihat perkembangan jumlah hunian di lahan permukiman terbangun ketika data eksisting mengenai jumlah hunian yang dibutuhkan untuk tahun dasar belum diperoleh selama pengumpulan data sekunder. Pentingnya data kuantitas hunian eksisting dalam kegiatan perencanaan tentu saja sebagai dasar untuk menentukan tingkat kekurangan hunian di masa depan (proyeksi). Tujuan dari penelitian ini adalah untuk menentukan data eksisting mengenai jumlah hunian pada tahun 2022 sebagai bentuk alternatif penyediaan kuantitas hunian di lahan permukiman terbangun yang akan digunakan sebagai data perencanaan di Kabupaten Musi Banyuasin. Metode yang digunakan adalah pendekatan kuantitatif dengan metode analisis, statistik, spasial, dan teknis. Berdasarkan hasil analisis, jumlah unit hunian di Kabupaten Musi Banyuasin pada lahan permukiman terbangun seluas 7.528 hektar menghasilkan 205.962 unit hunian yang tersebar di seluruh kecamatan dengan rata-rata luas hunian sebesar 148 meter persegi per rumah yang terdiri dari 124.441 rumah tangga. Hasil ini didasarkan pada penilaian terhadap nilai standar deviasi dan tingkat korelasi dari 3 data perbandingan alternatif, yaitu 213.645 unit hunian dan 243.227 unit hunian, di mana keduanya memiliki nilai standar deviasi dan korelasi di bawah nilai alternatif 1.Kata kunci: Permukiman; Hunian; Keputusan; Statistik; Teknis

Statistical Model Checking : An Overview

Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas; the algorithms themselves depend on the class of systems being analyzed as well as the logic used for specifying the properties. Another approach to solve the model checking problem is to \emph{simulate} the system for finitely many runs, and use \emph{hypothesis testing} to infer whether the samples provide a \emph{statistical} evidence for the satisfaction or violation of the specification. In this short paper, we survey the statistical approach, and outline its main advantages in terms of efficiency, uniformity, and simplicity.Comment: non