scholarly journals ALF-Score - a Novel Approach to Build a Predictive Network-Based Walkability Scoring System

2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller
Keyword(s):  
Spatial Resolution ◽  
Road Network ◽  
High Spatial Resolution ◽  
Ground Truth ◽  
High Confidence ◽  
The Road ◽  
Points Of Interest ◽  
Novel Approach ◽  
Novel Method ◽  
Feature Score

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.

ALF-Score: Network-Based Walkability

2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller
Keyword(s):  
Public Health ◽  
Spatial Resolution ◽  
Road Network ◽  
High Spatial Resolution ◽  
Ground Truth ◽  
Active Living ◽  
High Confidence ◽  
Core Component ◽  
Novel Method ◽  
Feature Score

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.

scholarly journals ALF-Score: Network-Based Walkability

2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Yuanzhu Chen ◽  
Daniel Fuller
Keyword(s):  
Road Network ◽  
Network Science ◽  
Ground Truth ◽  
Network Embedding ◽  
High Confidence ◽  
Core Component ◽  
The Road ◽  
Conceptual Definition ◽  
User Data ◽  
Feature Score

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.

ALF-Score+ - Personalization of a Predictive Network-Based Walkability Scoring System

2021 ◽  
Author(s):  
Ali M. S. Alfosool ◽  
Daniel Fuller ◽  
Yuanzhu Chen
Keyword(s):  
Road Network ◽  
Strong Association ◽  
Social Environments ◽  
Points Of Interest ◽  
Novel Approach ◽  
System P ◽  
Single Score ◽  
Cluster Profiles ◽  
Us Cities ◽  
Feature Score

Measuring environments around us (cities, roads, social environments) is crucial to understand human behaviour and help predict how aspects of environment influence behaviour and health. Walkability is one measure of environment used to predict health. Walkability combines aspects of environment (population, roads, amenities) into a single score. Existing measures are often one-size-fits-all with very limited personalization. In our previous work, we defined Active Living Feature Score, ALF-Score, a novel approach to measure network-based walkability. ALF-Score uses road network structures and points of interest to generate models capable of estimating walkability for any point on map. One of ALF-Score's contributions was the inclusion of user opinions to partially address the different perception among individuals and help derive a more personalized walkability score. Here, we take this personalization much further by introducing ALF-Score+ which uses individual user demographics (age, gender, ...) grouped using k-means and t-distributed stochastic neighbor embedding to create clusters based on individuals’ demographic characteristics. Each cluster is treated as a single profile representing a subset of users. Cluster profiles are added into our pipelines to generate profile-specific network-based walkability models. Results show strong variability among scores generated for each cluster profile with a clear variation in walkability generated for different users within same clusters. ALF-Score+ maintains an accuracy of 90.48% on average showing improvement compared to ALF-Score. We found strong association between cluster profiles' demographics and their scores. ALF-Score+ shows promising results providing personalized walkability based on cluster profiles, instead of a one-size-fits-all approach used by other walkability measures.

scholarly journals Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1737 ◽  
Author(s):  
Jinchao Song ◽  
Tao Lin ◽  
Xinhu Li ◽  
Alexander V. Prishchepov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
Remote Sensing Imagery ◽  
Points Of Interest ◽  
Very High Spatial Resolution ◽  
Functional Zones ◽  
Very High

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

scholarly journals Practical example for use of the supervised vicarious calibration (SVC) method on multisource hyperspectral imagery data – ValCalHyp airborne hyperspectral campaign under the EUFAR framework

2014 ◽  
Vol XL-7 ◽  
pp. 51-53
Author(s):  
A. Brook ◽  
E. Ben Dor
Keyword(s):  
Atmospheric Correction ◽  
Ground Truth ◽  
Critical Issue ◽  
Radiometric Calibration ◽  
Vicarious Calibration ◽  
Flight Direction ◽  
Novel Approach ◽  
Novel Method ◽  
General Goal ◽  
Cross Calibration

A novel approach for radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data, termed supervised vicarious calibration (SVC) was proposed by Brook and Ben-Dor in 2010. The present study was aimed at validating this SVC approach by simultaneously using several different airborne HSR sensors that acquired HSR data over several selected sites at the same time. The general goal of this study was to apply a cross-calibration approach to examine the capability and stability of the SVC method and to examine its validity. This paper reports the result of the multi sensors campaign took place over Salon de Provenance, France on behalf of the ValCalHyp project took place in 2011. The SVC method enabled the rectification of the radiometric drift of each sensor and improves their performance significantly. The flight direction of the SVC targets was found to be a critical issue for such correction and recommendations have been set for future utilization of this novel method. The results of the SVC method were examined by comparing ground-truth spectra of several selected validation targets with the image spectra as well as by comparing the classified water quality images generated from all sensors over selected water bodies.

scholarly journals Tiling light sheet selective plane illumination microscopy using discontinuous light sheets

2018 ◽  
Author(s):  
Liang Gao
Keyword(s):  
Spatial Resolution ◽  
3D Imaging ◽  
High Spatial Resolution ◽  
Image Data ◽  
Image Plane ◽  
Light Sheet ◽  
Selective Plane Illumination Microscopy ◽  
Novel Method ◽  
Technical Details ◽  
Imaging Speed

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.

scholarly journals Application of AHP to Road Selection

2020 ◽  
Vol 9 (2) ◽  
pp. 86 ◽  
Author(s):  
Yuan Han ◽  
Zhonghui Wang ◽  
Xiaomin Lu ◽  
Bowei Hu
Keyword(s):  
Decision Making ◽  
Road Network ◽  
Analytic Hierarchy ◽  
Ahp Model ◽  
The Road ◽  
Points Of Interest ◽  
Adaptive Thresholds ◽  
Contextual Characteristics ◽  
Hierarchy Process ◽  
Contextual Characteristic

The analytic hierarchy process (AHP), a decision-making method, allows the relative prioritization and assessment of alternatives under multiple criteria contexts. This method is also well suited for road selection. The method for road selection based on AHP involves four steps: (i) Points of Interest (POIs), the point-like representations of the facilities and habitations in maps, are used to describe and build the contextual characteristic indicator of roads; (ii) form an AHP model of roads with topological, geometrical, and contextual characteristic indicators to calculate their importance; (iii) select roads based on their importance and the adaptive thresholds of their constituent density partitions; and (iv) maintain the global connectivity of the selected network. The generalized result at a scale of 1:200,000 by AHP-based methods better preserved the structure of the original road network compared with other methods. Our method also gives preference to roads with relatively significant contextual characteristics without interfering with the structure of the road network. Furthermore, the result of our method largely agrees with that of the manual method.

A Novel Approach to Approximate Crime Hotspots to the Road Network

2019 ◽  
Author(s):  
Francisco C. F. Nunes Junior ◽  
Ticiana L. Coelho da Silva ◽  
José F. de Queiroz Neto ◽  
José Antônio F. de Macêdo ◽  
Wellington Clay Porcino
Keyword(s):  
Road Network ◽  
The Road ◽  
Novel Approach

scholarly journals Defining high-flow seasons using temporal streamflow patterns from a global model

2015 ◽  
Vol 19 (11) ◽  
pp. 4689-4705 ◽  
Author(s):  
D. Lee ◽  
P. Ward ◽  
P. Block
Keyword(s):  
Natural Hazards ◽  
Spatial Resolution ◽  
Early Warning ◽  
Performance Metrics ◽  
High Spatial Resolution ◽  
High Flow ◽  
Novel Approach ◽  
Basin Scale ◽  
Threshold Technique

Abstract. Globally, flood catastrophes lead all natural hazards in terms of impacts on society, causing billions of dollars of damages annually. Here, a novel approach to defining high-flow seasons (3-month) globally is presented by identifying temporal patterns of streamflow. The main high-flow season is identified using a volume-based threshold technique and the PCR-GLOBWB model. In comparison with observations, 40 % (50 %) of locations at a station (sub-basin) scale have identical peak months and 81 % (89 %) are within 1 month, indicating fair agreement between modeled and observed high-flow seasons. Minor high-flow seasons are also defined for bi-modal flow regimes. Identified major and minor high-flow seasons together are found to well represent actual flood records from the Dartmouth Flood Observatory, further substantiating the model's ability to reproduce the appropriate high-flow season. These high-spatial-resolution high-flow seasons and associated performance metrics allow for an improved understanding of temporal characterization of streamflow and flood potential, causation, and management. This is especially attractive for regions with limited observations and/or little capacity to develop early warning flood systems.

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