János Tóth, Domokos Esztergár-Kiss

Smart City

Consider more than just the numbers

A key danger in Big Data is a ‘selection bias’ in which the samples do not represent a cross section of the population. For example, mining data on Twitter will be biased in favor of young people most likely living in urban areas, because they make up most of Twitter’s users. So analyses based on Big Data may lack ‘external validity’. Another risk is that analyses based on Big Data will focus too much on correlation and prediction at the expense of cause, diagnostics, or inference. Since 2010, some US cities have utilized ‘Predictive policing’, meaning that police and law enforcement entities mine and analyze data to assess the likelihood of increased crime in certain areas, predicting rises based on historical patterns [11]. Forces dispatch their resources accordingly, and in most cases this has reduced crime. However, this type of knowledge cannot replace preventive policy that tackles the root causes and contributing factors. The visualization of the data is important as it will enhance the understanding of the findings. It provides a way to determine where to look and what questions to ask. ‎[33]

Tartalomjegyzék

Kiadó: Akadémiai Kiadó

Online megjelenés éve: 2019

ISBN: 978 963 454 271 1

This course material is included in the BME Faculty of Transportation Engineering and Vehicle Engineering Master programme. The main topics of Smart City course are the followings: Paradigm shift in urban citizen’s life, Smart city introduction, definitions and evaluation methods, Land use functions and models, city planning and strategic aspects, Utilization possibilities of information from social media, Internet of Things, wireless sensor networks and Smart Grid applications, Intermodal connections with their functionalities in the Smart City, Smart solutions in transportation management, Hungarian and international best practices.

Hivatkozás: https://mersz.hu/toth-esztergar-kiss-smart-city//

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