A big data approach to understanding pedestrian route choice preferences: Evidence from San Francisco

2021 ◽  
Vol 25 ◽  
pp. 41-51
Author(s):  
Andres Sevtsuk ◽  
Rounaq Basu ◽  
Xiaojiang Li ◽  
Raul Kalvo
Keyword(s):  
Big Data ◽  
San Francisco ◽  
Route Choice

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 Predicting Travel Behaviour of International and Domestic Tourists using Big Data

2019 ◽  
Vol 8 (3) ◽  
pp. 1572-1580
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
San Francisco ◽  
Data Analytics ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Travel Behaviour ◽  
Tourism Industry ◽  
Machine Learning Algorithms ◽  
Data Set

Tourism is one of the most important sectors contributing towards the economic growth of India. Big data analytics in the recent times is being applied in the tourism sector for the activities like tourism demand forecasting, prediction of interests of tourists’, identification of tourist attraction elements and behavioural patterns. The major objective of this study is to demonstrate how big data analytics could be applied in predicting the travel behaviour of International and Domestic tourists. The significance of machine learning algorithms and techniques in processing the big data is also important. Thus, the combination of machine learning and big data is the state-of-art method which has been acclaimed internationally. While big data analytics and its application with respect to the tourism industry has attracted few researchers interest in the present times, there have been not much researches on this area of study particularly with respect to the scenario of India. This study intends to describe how big data analytics could be used in forecasting Indian tourists travel behaviour. To add much value to the research this study intends to categorize on what grounds the tourists chose domestic tourism and on what grounds they chose international tourism. The online datasets on places reviews from cities namely Chicago, Beijing, New York, Dubai, San Francisco, London, New Delhi and Shanghai have been gathered and an associative rule mining based algorithm has been applied on the data set in order to attain the objectives of the study

scholarly journals From Committees of RUSA: Reflections on the RUSA President’s Program 2015: It’s Complicated: Navigating the Dynamic Landscapes of Digital Literacy, Collapsing Contexts, and Big Data

2016 ◽  
Vol 55 (3) ◽  
pp. 241
Author(s):  
Cathay Keough
Keyword(s):  
Big Data ◽  
Data Collection ◽  
San Francisco ◽  
Digital Literacy ◽  
Annual Conference ◽  
Personal Privacy ◽  
The People ◽  
The World ◽  
Dynamic Landscapes

There comes a time when a researcher speaks to librarians and the aftermath of the articulation echoes for days, maybe even weeks. danah boyd’s rapid-paced, information-packed RUSA President’s Program presentation at the ALA Annual Conference 2015 in San Francisco resonated around three topics:How technology can complicate our understanding of the world and the people around us by presenting information outside of its original contextHow technology can expand the ways in which we understand the world and the people around us by bringing us into contact with ideas, cultures, and contexts that we would otherwise be unawareHow increased data collection, and issues of classification, storage, and access are creating challenges to personal privacy 

A GPS-based bicycle route choice model for San Francisco, California

2011 ◽  
Vol 3 (1) ◽  
pp. 63-75 ◽  
Author(s):  
Jeffrey Hood ◽  
Elizabeth Sall ◽  
Billy Charlton
Keyword(s):  
San Francisco ◽  
Choice Model ◽  
Route Choice

scholarly journals An Optimized Machine Learning and Big Data Approach to Crime Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ashokkumar Palanivinayagam ◽  
Siva Shankar Gopal ◽  
Sweta Bhattacharya ◽  
Noble Anumbe ◽  
Ebuka Ibeke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
San Francisco ◽  
Learning Algorithm ◽  
Important Research ◽  
Feature Generation ◽  
Crime Detection ◽  
The Subject ◽  
Bayes Algorithm ◽  
Analyse Data

Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning-based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis to increase the accuracy of the subject machine learning algorithm. We implemented our proposed methodology using two standard datasets. Results show that the proposed feature generation method increased the performance of machine learning models. The highest accuracy of 97.5% was obtained when the proposed methodology was applied to the Naïve Bayes algorithm while analysing the San Francisco dataset.

Preserving Privacy with Federated Learning in Route Choice Behavior Modeling

2021 ◽  
pp. 036119812110111
Author(s):  
Yonghyeon Kweon ◽  
Bingrong Sun ◽  
B. Brian Park
Keyword(s):  
Big Data ◽  
Choice Behavior ◽  
Route Choice ◽  
Behavior Modeling ◽  
Model Parameters ◽  
Learning Approach ◽  
Privacy Concerns ◽  
Route Choice Behavior ◽  
One Year ◽  
Similar Accuracy

While big data helps improve decision-making and model developments, it often runs into privacy concerns. An example would be retrieving drivers’ origin and destination information from smartphone navigation apps for developing a route choice behavior model. To conserve privacy, yet to take advantage of big data in navigation applications, the authors propose to apply a federated learning approach, which has shown promising application in predicting smartphone keyboard’s next word without sending text to the server. Additional benefits of using federated learning is to save on data communications, by sending model parameters instead of entire raw data, and to distribute the computational burden to each smartphone instead of to the main server. The results from real-world route navigation usage data from about 30,000 drivers over one year showed that the proposed federated learning approach was able to achieve very similar accuracy to the traditional centralized global model and yet assures privacy.

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