Lecture

Mobilities through Big Data

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Description

This lecture explores the impact of Big Data on mobility planning, discussing the historical milestones reached in 2008, the three properties of Big Data (Volume, Variety, Velocity), and the political challenges related to data management. It delves into how Big Data enables the measurement of various mobilities, the identification of patterns for transport optimization, and the association of structured and unstructured data from diverse sources. The promises and limitations of Big Data in mobility are also analyzed, emphasizing the need for detailed data analysis. The presentation concludes by highlighting the importance of entering data and calculations for effective mobility planning.

In MOOC

Mobility planning

Beyond transportation, mobility encompasses a multitude of phenomena and realities that are constantly changing and interacting. This implies political choices over time, which are at the heart of the planning exercise. Over the course of 5 weeks and with the participation of numerous guest speakers, the course will provide you with theoretical elements, tools, techniques and practical examples to help you undertake a mobility planning process. This MOOC is the product of a collaboration between two resea

Official source

Related concepts (32)

Big data

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.

Data analysis

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Unstructured data

Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents.

Exploratory data analysis

In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling and thereby contrasts traditional hypothesis testing. Exploratory data analysis has been promoted by John Tukey since 1970 to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments.

Data

In common usage and statistics, data (USˈdætə; UKˈdeɪtə) is a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures.

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