scholarly journals Transport System Models and Big Data: Zoning and Graph Building with Traditional Surveys, FCD and GIS

2019 ◽  
Vol 8 (4) ◽  
pp. 187 ◽  
Author(s):  
Croce ◽  
Musolino ◽  
Rindone ◽  
Vitetta
Keyword(s):  
Big Data ◽  
Transport System ◽  
Census Data ◽  
Small Data ◽  
Mobility Patterns ◽  
Urban Context ◽  
System Models ◽  
Floating Car Data ◽  
Information And Communication ◽  
Current Stage

The paper deals with the integration of data provided from traditional transport surveys (small data) with big data, provided from Information and Communication Technology (ICT), in building Transport System Models (TSMs). Big data are used to observe historical mobility patterns and transport facilities and services, but they are not able to assess ex-ante effects of planned interventions and policies. To overcome these limitations, TSMs can be specified, calibrated and validated with small data, but they are expensive to obtain. The paper proposes a procedure to increase the benefits of TSMs’ building in forecasting capabilities, on one side; and limiting the costs connected to traditional surveys thanks to the availability of big data, on the other side. Small data (e.g., census data) are enriched with Floating Car Data (FCD). At the current stage, the procedure focuses on two specific elements of TSMs: zoning and graph building. These processes are both executed considering the estimated values of an intensity function of FCDs, consistently with traditional methods based on small data. The data-fusion of small and big data, operated with a Geographic Information System (GIS) tool, in a real extra-urban context is presented in order to validate the proposed procedure.

scholarly journals Estimation of Travel Demand Models with Limited Information: Floating Car Data for Parameters’ Calibration

2021 ◽  
Vol 13 (16) ◽  
pp. 8838
Author(s):  
Antonello Ignazio Croce ◽  
Giuseppe Musolino ◽  
Corrado Rindone ◽  
Antonino Vitetta
Keyword(s):  
Big Data ◽  
Transport System ◽  
Systems Engineering ◽  
Travel Demand ◽  
Transport Systems ◽  
Limited Information ◽  
Sources Of Information ◽  
Floating Car Data ◽  
Demand Models ◽  
System Configurations

This paper attempts to integrate data from models, traditional surveys and big data in a situation of limited information. The goal is to increase the capacity of transport planners to analyze, forecast, and plan passenger mobility. (Big) data are a precious source of information and substantial effort is necessary to filter, integrate, and convert big data into travel demand estimates. Moreover, data analytics approaches without demand models are limited because they allow: (a) the analysis of historical and/or real-time transport system configurations, and (b) the forecasting of transport system configurations in ordinary conditions. Without the support of travel demand models, the mere use of (big) data does not allow the forecasting of mobility patterns. The paper attempts to support traditional methods of transport systems engineering with new data sources from ICTs. By combining traditional data and floating car data (FCD), the proposed framework allows the estimation of travel demand models (e.g., trip generation and destination). The proposed method can be applied in a specific case of an area where FCD are available, and other sources of information are not available. The results of an application of the proposed framework in a sub-regional area (Calabria, southern Italy) are presented.

scholarly journals Monitoring streets through tweets: Using user-generated geographic information to predict gentrification and displacement

2021 ◽  
pp. 239980832110253
Author(s):  
Karen Chapple ◽  
Ate Poorthuis ◽  
Matthew Zook ◽  
Eva Phillips
Keyword(s):  
Big Data ◽  
San Francisco ◽  
Census Data ◽  
San Francisco Bay Area ◽  
Demographic Data ◽  
Activity Patterns ◽  
Neighborhood Change ◽  
Housing Affordability ◽  
Bay Area ◽  
Media Data

The new availability of big data sources provides an opportunity to revisit our ability to predict neighborhood change. This article explores how data on urban activity patterns, specifically, geotagged tweets, improve the understanding of one type of neighborhood change—gentrification—by identifying dynamic connections between neighborhoods and across scales. We first develop a typology of neighborhood change and risk of gentrification from 1990 to 2015 for the San Francisco Bay Area based on conventional demographic data from the Census. Then, we use multivariate regression to analyze geotagged tweets from 2012 to 2015, finding that outsiders are significantly more likely to visit neighborhoods currently undergoing gentrification. Using the factors that best predict gentrification, we identify a subset of neighborhoods that Twitter-based activity suggests are at risk for gentrification over the short term—but are not identified by analysis with traditional census data. The findings suggest that combining Census and social media data can provide new insights on gentrification such as augmenting our ability to identify that processes of change are underway. This blended approach, using Census and big data, can help policymakers implement and target policies that preserve housing affordability and protext tenants more effectively.

scholarly journals Managing food security through food waste and loss: Small data to big data

2018 ◽  
Vol 98 ◽  
pp. 367-383 ◽  
Author(s):  
Zahir Irani ◽  
Amir M. Sharif ◽  
Habin Lee ◽  
Emel Aktas ◽  
Zeynep Topaloğlu ◽  
...  
Keyword(s):  
Big Data ◽  
Food Security ◽  
Food Waste ◽  
Small Data

scholarly journals Big Data Versus Small Data: The Case of ‘Gripe’ (Flu) in Spanish

2015 ◽  
Vol 198 ◽  
pp. 339-343 ◽  
Author(s):  
Antonio Moreno-Sandoval ◽  
Esteban Moro
Keyword(s):  
Big Data ◽  
Small Data

scholarly journals Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives

2014 ◽  
Vol 23 (01) ◽  
pp. 21-26 ◽  
Author(s):  
T. Miron-Shatz ◽  
A. Y. S. Lau ◽  
C. Paton ◽  
M. M. Hansen
Keyword(s):  
Health Care ◽  
Big Data ◽  
Health Care Providers ◽  
Data Privacy ◽  
Recent Literature ◽  
Unstructured Data ◽  
Small Scale ◽  
Small Data ◽  
Quantified Self ◽  
Care Providers

Summary Objectives: As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. Methods: A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Results: Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful.

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