Trabajo final del nucleo 2 de crecimiento y desarrollo para Lic en economia de la universidad nacional del comahue, con aportes del nucleo 3 como concepto de crisis capitalista

INTRODUCTION TO CODING FOR MEDIA DATA ANALYSIS 과제

This is an interactive map presenting the crash frequency in the section between km 319.8 and km 322 of the BR-101 Federal Highway in the city of Niteroi before the roadworks of an additional lane for each traffic bound had begun. The dataset comprises the period between June 28th, 2008, and January 31st, 2013. A Kernel Estimator was used to illustrate the occurrence of crash hotspots, while the georeferenced red dots carry valuable information on the number of accidents per type (i.e., Property Damage Only, Injury Crash, Pedestrian-Injury Crash, and Fatal Crash) occurring along the road. Finally, the sum of Standard Severity Units of each point is calculated according to the methodology proposed by the Brazilian National Plan of Crash Control and Traffic Safety, which is presented as follows: SSU = sum(1 x PDO + 4 x Injury Crash + 6 x Pedestrian-Injury Crash + 13 x Fatal Cash) Where: SSU: the number of Standard Severity Units at a particular location; PDO: the number of Property Damage Only crashes; Injury Crash: the number of crashes where at least one person other than a pedestrian got injured; Pedestrian-Injury Crash: the number of accidents where at least one pedestrian got injured; Fatal Crash: the number of accidents with at least one death.

2025-1 코딩 기말과제

Rindra Satriatama Putra Muhammad Rizki Fajar Ananta Galih Mahardika Muhammad Faried Risky Feriawan Muhammad Ridho Alghifari

It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a Fitbit, Nike Fuelband, or Jawbone Up. These type of devices are part of the "quantified self" movement -- a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data.

This is an interactive map presenting the crash frequency in the section between km 319.8 and km 322 of the BR-101 Federal Highway in the city of Niteroi after the inauguration of an additional lane for each traffic bound. A Kernel Estimator was used to illustrate the occurrence of crash hotspots, while the georeferenced red dots carry valuable information on the number of accidents per type (i.e., Property Damage Only, Injury Crash, Pedestrian-Injury Crash, and Fatal Crash) occurring along the road. Finally, the sum of Standard Severity Units of each point is calculated according to the methodology proposed by the Brazilian National Plan of Crash Control and Traffic Safety, which is presented as follows: SSU = sum(1 x PDO + 4 x Injury Crash + 6 x Pedestrian-Injury Crash + 13 x Fatal Cash) Where: SSU: the number of Standard Severity Units at a particular location; PDO: the number of Property Damage Only crashes; Injury Crash: the number of crashes where at least one person other than a pedestrian got injured; Pedestrian-Injury Crash: the number of accidents where at least one pedestrian got injured; Fatal Crash: the number of accidents with at least one death.

Ini adalah projek akhir untuk penilaian, berisi jawaban dan perhitungan untuk uji hipotesis dari kelompok 2