The neighbourhood built environment affects driving behaviours of older adults: a combined geographic information

Rayeheh Hafezifar1, Shahab Alizadeh1,2, Anne Dickerson3

  • 1Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.

Cities & Health
|January 19, 2026
PubMed

Related Concept Videos

Drosophila Adult Olfactory Shock Learning09:48

Drosophila Adult Olfactory Shock Learning

The method to measure adult Drosophila associative memory is described. The assay is based on the ability of the fly to associate an odor presented with a negative reinforcer (electric shock) and then recall this information at a later time, allowing memory to be...
29.1K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Here, we present a protocol for preparing and culturing a blood brain barrier metastatic tumor micro-environment and then quantifying its state using confocal imaging and artificial intelligence (machine...
7.7K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

This study evaluates prognostic systems for colorectal signet-ring cell carcinoma patients using machine learning models and competing risk analyses. It identifies log odds of positive lymph nodes as a superior predictor compared to pN staging, demonstrating strong predictive performance and aiding clinical decision-making through robust survival prediction...
495
Constructing and Visualizing Models using Mime-based Machine-learning Framework06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.
2.3K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

This protocol describes a driving simulation platform and a tactile vibrating toolkit for the investigation of driving-related research. An exemplar experiment exploring the effectiveness of tactile warnings is also...
4.9K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and...
939