Traveling Alone or Together? Neighborhood Context on Individual and Group Juvenile and Adult Burglary Decisions

Neighborhood characteristics are important considerations when offenders make targeting decisions. Movement patterns among adults and juveniles vary widely, which impacts both the number of crime opportunities and the range of neighborhoods to which an offender is exposed. We test whether offending patterns among adult and juvenile burglars vary based on distances traveled, the types of neighborhoods targeted, and whether suspects acted alone or in a group. Using discrete choice modeling, we draw upon a unique sample of cleared burglaries in a representative city in the south over a 13-year period. Results show that adult burglars consistently travel further and are more sensitive to neighborhood conditions than their juvenile counterparts, but that group participation makes little difference in target decisions.

Giá trị kinh tế của hệ sinh thái Vườn Quốc gia U Minh Hạ đối với người dân huyện Trần Văn Thời, tỉnh Cà Mau

Bài viết nhằm xác định giá trị kinh tế của hệ sinh thái Vườn Quốc gia U Minh Hạ thông qua ước tính mức sẵn lòng đóng góp của người dân huyện Trần Văn Thời, tỉnh Cà Mau cho dự án bảo tồn rừng bằng phương pháp mô hình lựa chọn (Choice Modeling). Kết quả mô hình ước lượng cho thấy người dân sẵn sàng trả thêm cho các lợi ích mà Vườn Quốc gia U Minh Hạ mang lại như làm tăng sản phẩm rừng, giảm mất đất rừng, và phát triển dịch vụ du lịch sinh thái. Đáp viên sẵn lòng đóng góp trung bình khoảng 0,5kg gạo hàng tháng để tăng thêm 10 năm cung cấp sản phẩm rừng, đóng góp khoảng 0,9kg gạo hàng tháng nếu dự án có thể làm giảm mất đất rừng 50%. Để tăng thêm dịch vụ du lịch sinh thái 15%, các đáp viên sẵn lòng đóng góp hàng tháng khoảng 1kg gạo. Những kết quả đạt được này rất hữu ích để đánh giá thực trạng và khả năng chi trả của người dân trong việc phát triển hệ sinh thái Vườn Quốc gia U Minh Hạ.

Menilai Pelestarian Lingkungan Pantai Berdasarkan Circular Economy dengan Studi Literatur Pendekatan Choice Modelling

Perilaku pengunjung wisata yang kurang baik dapat menyebabkan kerusakan lingkungan pantai yang disebabkan karena penumpukan sampah-sampah. Disisi lain, minimnya kesadaran pengunjung dan masyarakat sekitar akan dampak yang dilakukan pada saat berkunjung ke pantai serta minimnya kesadaran dalam pelestarian lingkungan pantai. Tujuan dari penelitian ini adalah untuk mengidentifikasi penerapan circular economy serta untuk mengetahui penggunaan pendekatan choice modelling dalam menilai pelestarian lingkungan pantai. Metode yang digunakan adalah metode riset studi literatur dengan pendekatan deskriptif. Pengumpulan data sekunder dilakukan dengan mengumpulkan informasi dan data dari berbagai sumber melalui media elektronik berbasis web, jurnal yang relevan, hasil penelitian, dan sebagainya. Hasil penelitian ini yaitu menilai valuasi ekonomi pelestarian lingkungan pantai dapat dilakukan dengan konsep circular economy dan menggunakan metode atau pendekatan choice modelling untuk melihat alternatif pilihan yang dipilih oleh pengunjung pantai. AbstractThe behavior of tourist visitors who are not good can cause damage to the beach environment caused by the accumulation of garbage. On the other hand, the lack of awareness of visitors and the surrounding community about the impacts made when visiting the beach and the lack of awareness in preserving the coastal environment. The purpose of this study is to identify the application of the circular economy and to determine the use of the choice modeling approach in assessing the preservation of the coastal environment. The method used is a literature study research method with a descriptive approach. Secondary data collection is done by collecting information and data from various sources through web-based electronic media, relevant journals, research results, and so on. The results of this study are to assess the economic valuation of coastal environmental conservation can be done with the concept of a circular economy and using a method or approach choice modeling to see alternative choices chosen by beach visitors.

Air Passengers’ Preferences for Aviation Voluntary Carbon Offsetting: A Co-benefits Perspective

Co-benefits are positive outcomes from voluntary carbon offsetting (VCO) programs beyond simple reduction in carbon emissions, which include biodiversity, air quality, economic, health, and educational benefits. Given the rates of aviation VCOs remain at less than 10%, this study investigated air passengers’ preferences for co-benefits as well as certification, location, and cost of VCO programs. Using discrete choice modeling, this study shows that aviation VCO programs with higher levels of co-benefits, particularly biodiversity and health benefits, are preferred by air passengers and confirms a preference for domestically based and certified VCO programs. The latent class choice model identified three classes with different preferences for VCO program attributes and demographic characteristics. The results of this study contribute to the knowledge of VCO co-benefits and imply that airlines should take note of this preference for biodiversity and health co-benefits when designing VCO programs and differentiate between market segments to increase the uptake of VCOs.

Customer Choice Modelling: A Multi-Level Consensus Clustering Approach

Customer Choice Modeling aims to model the decision-making process of customers, or segments of customers, through their choices and preferences identified by the analysis of their behaviors in one or more specific contexts. Clustering techniques are used in this context to identify patterns in their choices and preferences, to define segments of customers with similar behaviors, and to model how customers of different segments respond to competing products and offers. However, data clustering is an unsupervised learning task by nature, that is the grouping of customers with similar behaviors in clusters must be performed without prior knowledge about the nature and the number of intrinsic groups of data instances, i.e., customers, in the data space. Thus, the choice of both the clustering algorithm used and its parameterization, and of the evaluation method used to assess the relevance of the resulting clusters are central issues. Consensus clustering, or ensemble clustering, aims to solve these issues by combining the results of different clustering algorithms and parameterizations to generate a more robust and relevant final clustering result. We present a Multi-level Consensus Clustering approach combining the results of several clustering algorithmic configurations to generate a hierarchy of consensus clusters in which each cluster represents an agreement between different clustering results. A closed sets based approach is used to identified relevant agreements, and a graphical hierarchical representation of the consensus cluster construction process and their inclusion relationships is provided to the end-user. This approach was developed and experimented in travel industry context with Amadeus SAS. Experiments show how it can provide a better segmentation, and refine the customer segments by identifying relevant sub-segments represented as sub-clusters in the hierarchical representation, for Customer Choice Modeling. The clustering of travelers was able to distinguish relevant segments of customers with similar needs and desires (i.e., customers purchasing tickets according to different criteria, like price, duration of flight, lay-over time, etc.) and at different levels of precision, which is a major issue for improving the personalization of recommendations in flight search queries.