Recommendations to Improve the Smartness of a City

Elsa Negre () and Camille Rosenthal-Sabroux ()
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Elsa Negre: Université Paris-Dauphine
Camille Rosenthal-Sabroux: Université Paris IX Dauphine

A chapter in Smart City, 2014, pp 101-115 from Springer

Abstract: Abstract The concept of “smart city” has not yet been clearly defined. However, there are six characteristics/categories for classifying this kind of cities and compare them: smart economy Smart economy , smart mobility, smart environment, smart people Smart people , smart living and smart governance. However, being “smart” is a challenge increasingly important for many cities or communities. This is of particular interest in the domain of Information and Communications Technology (ICT) and for such systems where there are economic, social, and other issues. To the best of our knowledge, there are no studies that attempt to help identifying the actions to be implemented to improve the smartness of a city. Recommending such actions is an emerging and promising field of investigation. Usually, recommender systems Recommender system try to predict the rating that a user would give to an item (such as music, books, …) he has not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user’s social environment (collaborative filtering approaches). In this chapter, we present a framework for a recommender system Recommender system for cities. The scope of this research work is to take advantage from recognized “smart cities” and to make same actions for city who wants to become “smart”. The followed method is: having a list of characteristics of a “smart city”, and having a city which wants to become “smart”, which actions must be implemented to become “smart” regarding the characteristics of “smartness”. This framework uses the actions already implemented in smart cities to enhance the smartness of a given city. The main idea is to recommend to the city the actions already implemented in those smart cities that are similar (the similarity between two cities is based on some indicators such as air quality, water consumption, etc.) as the actions to be implemented in the said city. This is done by (1) Pre-treating the indicators values of a given smart city category (only one among the six), (2) Matching the indicators corresponding to this category, (3) Returning to the city the actions to be implemented in a given order (according to the preferences of the city which needs help, for example). Thus, the city will be able to improve its smartness.

Keywords: Information systems; Recommender systems; Smart cities (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-319-06160-3_5

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DOI: 10.1007/978-3-319-06160-3_5

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