ALF-Score: Network-Based Walkability

Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller

Abstract Walkability is a term that describes various aspects of the built and social environment and has been associated with physical activity and public health. Walkability is subjective and although multiple definitions of walkability exist, there is no single agreed upon definition. Road networks are integral parts of mobility and should be an important part of walkability. However, they are missing from existing methods. Most walkability measures only provide area-based scores with low spatial resolution, have a one-size-fits-all approach, and do not consider individuals opinion. Active Living Feature Score (ALF-Score) is a network-based walkability measure that incorporates road network structures as a core component. It also utilizes user opinion to build a high-confidence ground-truth that is used in our machine learning pipeline to generate models capable of estimating walkability. We found combination of network features with road embedding and POI features creates a complimentary feature set enabling us to train our models with an accuracy of over 87% while maintaining a conversion consistency of over 98%. Our proposed approach outperforms existing measures by introducing a novel method to estimate walkability scores that are representative of users opinion with a high spatial resolution, for any point on the map.

2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller

Abstract Walkability is a term that describes various aspects of the built and social environment and has been associated with physical activity and public health. Walkability is subjective and although multiple definitions of walkability exist, there is no single agreed upon definition. Road networks are integral parts of mobility and should be an important part of walkability. However, using the road structure as nodes is not widely discussed in existing methods. Most walkability measures only provide area-based scores with low spatial resolution, have a one-size-fits-all approach, and do not consider individuals opinion. Active Living Feature Score (ALF-Score) is a network-based walkability measure that incorporates road network structures as a core component. It also utilizes user opinion to build a high-confidence ground-truth that is used in our machine learning pipeline to generate models capable of estimating walkability. We found combination of network features with road embedding and points of interest features creates a complimentary feature set enabling us to train our models with an accuracy of over 87% while maintaining a conversion consistency of over 98%. Our proposed approach outperforms existing measures by introducing a novel method to estimate walkability scores that are representative of users opinion with a high spatial resolution, for any point on the map.


2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller

Walkability is a term that describes aspects of the built and social environment. Previous studies have shown that different operationalisations of walkability are associated with physical activity and health. Walkability can be subjective and although multiple operational definitions and walkability measurement exist, there is no single agreed upon conceptual definition. Despite lack of consensus of a walkability definition, typical operational definitions include measures of population density, destinations, and the road network. Network science approaches such centralities and network embedding are missing from existing methods, yet they are integral parts of our mobility and should be an important part of how walkability is measured. Furthermore, most walkability measures have a one-size-fits-all approach and do not take into account individual user’s characteristics or walking preferences. To address some limitations of previous works, we developed the Active Living Feature Score (ALF-Score). ALF-Score is a network-based walkability measure that incorporates the road network structures as a core component. It also utilizes user data to build high-confidence ground truth that are used in conjunction with our machine learning pipeline to generate models capable of estimating walkability scores that address existing gaps in the walkability literature. We find, relying on road structure alone, we are able to train our models to estimate walkability scores with an accuracy of over 86% while maintaining a consistency of over 98% over collected user data. Our proposed approach outperforms existing measures by providing a walkability data at a much higher resolution as well as a user-derived result.


2018 ◽  
Author(s):  
Liang Gao

AbstractTiling light sheet selective plane illumination microscopy (TLS-SPIM) improves 3D imaging ability of SPIM by using a real-time optimized tiling light sheet. However, the imaging speed decreases, and size of the raw image data increases proportionally to the number of tiling positions in TLS-SPIM. The decreased imaging speed and the increased raw data size could cause significant problems when TLS-SPIM is used to image large specimens at high spatial resolution. Here, we present a novel method to solve the problem. Discontinuous light sheets created by scanning coaxial beam arrays synchronized with camera exposures are used for 3D imaging to decrease the number of tiling positions required at each image plane without sacrificing the spatial resolution. We investigate the performance of the method via numerical simulation and discuss the technical details of the method.


Author(s):  
A. B. Murynin ◽  
A. A. Richter ◽  
M. A. Shakhramanyan

The paper deals with the problem of integrated interpretation of waste disposal facilities according to satellite imagery and ground truth monitoring, features of space images of landfills from various points of view: texture analysis, statistical properties, fractal analysis, color features, and the possibility of using machine learning methods. The main visual interpretive signs of landfills on optical and radar images of high spatial resolution are given. The fractal dimension of landfills was calculated for high resolution images using two models.


1999 ◽  
Vol 29 (10) ◽  
pp. 1464-1478 ◽  
Author(s):  
Tomas Brandtberg

Individual tree based forest surveys are feasible using modern computer technology. The presented approach for analysing high spatial resolution (pixel size 10 cm) aerial images of naturally regenerated boreal forests is based on visible significant trees. Sunlight patches on the ground are suppressed, followed by optimal image smoothing. The problem with inclined illumination is handled by adapted thresholding. Each connected threshold segment (a collection of one or more trees) is further smoothed. A selection of the resulting convex edge segments is used for identifying significant tree crown circles. Six complementary image variables are estimated and used for regression analysis. An evaluation of the ground-truth data in central Sweden gives good results on the stem position estimate (a root mean square (RMS) error of 108 cm) and the stem number estimate (a relative RMS error of 11%). The complementary variables contribute significantly to the stem diameter prediction, resulting in the following experimental values: Scots pine (Pinus sylvestris L.) (R2 = 59.5%, s = 4.9 cm, N = 157), Norway spruce (Picea abies (L.) Karst.) (R2 = 21.9%, s = 6.4 cm, N = 398), birch (Betula pubescens Ehrh.) (R2 = 35.4%, s = 5.3 cm, N = 133), and European aspen (Populus tremula L.) (R2 = 61.4%, s = 4.6 cm, N = 13). The results indicate strong species dependence.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yechao Yan ◽  
Yangyang Xu ◽  
Shuping Yue

AbstractThermal stress poses a major public health threat in a warming world, especially to disadvantaged communities. At the population group level, human thermal stress is heavily affected by landscape heterogeneities such as terrain, surface water, and vegetation. High-spatial-resolution thermal-stress indices, containing more detailed spatial information, are greatly needed to characterize the spatial pattern of thermal stress to enable a better understanding of its impacts on public health, tourism, and study and work performance. Here, we present a 0.1° × 0.1° gridded dataset of multiple thermal stress indices derived from the newly available ECMWF ERA5-Land and ERA5 reanalysis products over South and East Asia from 1981 to 2019. This high-spatial-resolution database of human thermal stress indices over South and East Asia (HiTiSEA), which contains the daily mean, maximum, and minimum values of UTCI, MRT, and eight other widely adopted indices, is suitable for both indoor and outdoor applications and allows researchers and practitioners to investigate the spatial and temporal evolution of human thermal stress and its impacts on densely populated regions over South and East Asia at a finer scale.


Sign in / Sign up

Export Citation Format

Share Document