scholarly journals A Satellite-Based Land Use Regression Model of Ambient NO2 with High Spatial Resolution in a Chinese City

2021 ◽  
Vol 13 (3) ◽  
pp. 397
Author(s):  
Lina Zhang ◽  
Changyuan Yang ◽  
Qingyang Xiao ◽  
Guannan Geng ◽  
Jing Cai ◽  
...  
Keyword(s):  
Land Use ◽  
Policy Development ◽  
Model Fitting ◽  
Ground Level ◽  
Epidemiological Studies ◽  
City Center ◽  
Land Use Regression ◽  
Emission Data ◽  
The City ◽  
Density Population

Previous studies have reported that intra-urban variability of NO2 concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO2 concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO2 concentrations at a city scale by including satellite-retrieved NO2 tropospheric column density, population density, traffic indicators, and NOx emission data. The R2 of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO2 variations at a 100 m × 100 m resolution and demonstrated that NO2 pollution in winter is 1.46 times higher than that in summer. NO2 concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO2 concentrations exceeding the annual-mean standard at 40 μg/m3 in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control.

scholarly journals The Neural Network Assisted Land Use Regression

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 452
Author(s):  
Jan Bitta ◽  
Vladislav Svozilík ◽  
Aneta Svozilíková Krakovská
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Linear Regression ◽  
Dispersion Model ◽  
Land Use Regression ◽  
Emission Data ◽  
Pollution Dispersion ◽  
Factors Affecting ◽  
Standard Linear

Land Use Regression (LUR) is one of the air quality assessment modelling techniques. Its advantages lie mainly in a much simpler mathematical apparatus, quicker and simpler calculations, and a possibility to incorporate more factors affecting pollutant concentration than standard dispersion models. The goal of the study was to perform the LUR model in the Polish-Czech-Slovakian Tritia region, to test two sets of pollution data input factors, i.e., factors based on emission data and pollution dispersion model results, to test regression via neural networks and compare it with standard linear regression. Both input datasets, emission data and pollution dispersion model results, provided a similar quality of results in the case when standard linear regression was used, the R2 of the models was 0.639 and 0.652. Neural network regression provided a significantly higher quality of the models, their R2 was 0.937 and 0.938 for the factors based on emission data and pollution dispersion model results respectively.

Localization of Relevant Urban Micro-Consolidation Centers for Last-Mile Cargo Bike Delivery Based on Real Demand Data and City Characteristics

2021 ◽  
pp. 036119812110363
Author(s):  
Christian Rudolph ◽  
Alexis Nsamzinshuti ◽  
Samuel Bonsu ◽  
Alassane Ballé Ndiaye ◽  
Nicolas Rigo
Keyword(s):  
Land Use ◽  
City Center ◽  
Analytical Hierarchical Process ◽  
Logistics Service ◽  
Last Mile ◽  
Logistics Service Provider ◽  
Cargo Bike ◽  
The City ◽  
Weight Allocation ◽  
Use Category

The use of cargo cycles for last-mile parcel distribution requires urban micro-consolidation centers (UMC). We develop an approach to localize suitable locations for UMCs with the consideration of three criteria: demand, land use, and type of road. The analysis considers metric levels (demand), linguistic levels (land use), and cardinal levels (type of road). The land-use category is divided into commercial, residential, mixed commercial and residential, and others. The type of road category is divided into bicycle road, pedestrian zone, oneway road, and traffic-calmed road. The approach is a hybrid multi-criteria analysis combining an Analytical Hierarchical Process (AHP) and PROMETHEE methods. We apply the approach to the city center of Stuttgart in Germany, using real demand data provided by a large logistics service provider. We compared different scenarios weighting the criteria differently with DART software. The different weight allocation results in different numbers of required UMCs and slightly different locations. This research was able to develop, implement, and successfully apply the proposed approach. In subsequent steps, stakeholders such as logistics companies and cities should be involved at all levels of this approach to validate the selected criteria and depict the “weight” of each criterion.

Understanding the Shared E-scooter Travels in Austin, TX

2020 ◽  
Vol 9 (2) ◽  
pp. 135 ◽  
Author(s):  
Junfeng Jiao ◽  
Shunhua Bai
Keyword(s):  
Higher Education ◽  
Land Use ◽  
Operation Time ◽  
Street Connectivity ◽  
Travel Patterns ◽  
City Center ◽  
University Of Texas ◽  
The University ◽  
The Relationship ◽  
The City

This paper investigated the travel patterns of 1.7 million shared E-scooter trips from April 2018 to February 2019 in Austin, TX. There were more than 6000 active E-scooters in operation each month, generating over 150,000 trips and covered approximately 117,000 miles. During this period, the average travel distance and operation time of E-scooter trips were 0.77 miles and 7.55 min, respectively. We further identified two E-scooter usage hotspots in the city (Downtown Austin and the University of Texas campus). The spatial analysis showed that more trips originated from Downtown Austin than were completed, while the opposite was true for the UT campus. We also investigated the relationship between the number of E-scooter trips and the surrounding environments. The results show that areas with higher population density and more residents with higher education were correlated with more E-scooter trips. A shorter distance to the city center, the presence of transit stations, better street connectivity, and more compact land use were also associated with increased E scooter usage in Austin, TX. Surprisingly, the proportion of young residents within a neighborhood was negatively correlated with E-scooter usage.

scholarly journals Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images

2019 ◽  
Vol 21 (7) ◽  
pp. 1825-1838 ◽  
Author(s):  
Yi Zhu ◽  
Xueqing Deng ◽  
Shawn Newsam
Keyword(s):  
Land Use ◽  
Ground Level ◽  
Land Use Classification ◽  
Fine Grained ◽  
The City

scholarly journals Land Use Regression Modelling of traffic-related noise in Athens, Greece for use in epidemiological studies

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Konstantina Dimakopoulou ◽  
Kleanthi Manika ◽  
Evangelia Samoli ◽  
Natasa Kalpourtzi ◽  
Giota Touloumi ◽  
...  
Keyword(s):  
Land Use ◽  
Epidemiological Studies ◽  
Land Use Regression ◽  
Regression Modelling

Using a Land Use Regression Model with Machine Learning to Estimate Ground Level PM2.5

2021 ◽  
pp. 116846
Author(s):  
Pei-Yi Wong ◽  
Hsiao-Yun Lee ◽  
Yu-Ting Zeng ◽  
Yinq-Rong Chern ◽  
Nai-Tzu Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Regression Model ◽  
Ground Level ◽  
Land Use Regression

scholarly journals Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

2020 ◽  
Author(s):  
Zhiyuan Li ◽  
Kin-Fai Ho ◽  
Steve Hung Lam Yim
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Measurement Data ◽  
Quality Measurement ◽  
Epidemiological Studies ◽  
Air Pollutant ◽  
Land Use Regression ◽  
Suspended Particulates ◽  
Northern Taiwan

Abstract. To provide long-term air pollutant exposure estimates for epidemiological studies, it is essential to test the feasibility of developing land-use regression (LUR) models using only routine air quality measurement data and to evaluate the transferability of LUR models between nearby cities. In this study, we develop and evaluate the intercity transferability of annual average LUR models for ambient respirable suspended particulates (PM10), fine suspended particulates (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in the Taipei–Keelung metropolitan area of northern Taiwan in 2019. Ambient PM10, PM2.5, NO2, and O3 measurements at 30 fixed-site stations were used as the dependent variables, and a total of 156 potential predictor variables in six categories (i.e., population density, road network, land-use type, normalized difference vegetation index, meteorology, and elevation) were extracted using buffer spatial analysis. The LUR models were developed using the supervised forward linear regression approach. The LUR models for ambient PM10, PM2.5, NO2, and O3 achieved relatively high prediction performance, with R2 and leave-one-out cross-validation (LOOCV) R2 values of > 0.72 and > 0.53, respectively. The intercity transferability of LUR models varied among the air pollutants, with transfer-predictive R2 values of > 0.62 for NO2 and

scholarly journals Risk Assessment of Shanghai Extreme Flooding Under the Land Use Change Scenario

2021 ◽  
Author(s):  
Xinmeng Shan ◽  
Jie Yin ◽  
Jun Wang
Keyword(s):  
Risk Assessment ◽  
Land Use ◽  
Land Use Change ◽  
Public Service ◽  
City Center ◽  
Industrial Land ◽  
Flood Risks ◽  
Extreme Flooding ◽  
The City ◽  
Huangpu River

Abstract Environmental changes have led to non-stationary flood risks in coastal cities. How to quantitatively characterize the future change trend and effectively adapt is a frontier scientific problem that needs to be solved urgently. To this end, this study uses the 2010 Shanghai land use data as the base and uses the GeoSOS-FLUS model to simulate future land use change scenarios (2030, 2050, and 2100). Based on the results of storm and flood numerical simulations, probabilistic risk, and other multidisciplinary methods, extreme storm and flood risks of various land uses (residential land, commercial and public service land, industrial land, transportation land, agricultural land, and other land) in Shanghai are analyzed and 4 adaptation strategies to deal with extreme flooding have been developed. The research results show that: 1) Under the two emission scenarios, residential, commercial and public service, and industrial land have the highest exposure assets. Under the RCP8.5 scenario, the exposure of assets in 2100, 2050, and 2030 will be 1.7 times, 1.5 times, and 1.3 times that in 2010 for 1/1000-year, respectively; the losses will be 2.7 times, 2.0 times, and 1.8 times that in 2010, respectively. 2) The spatial pattern of loss, which forms the scattered distribution of 1/10-year, is mainly distributed on both sides of the Huangpu River. For 1/1000-year, which is mainly gradually showed a strip distribution, continuous distribution of the city center, and the Qingpu-Songjiang depression in the southwest are high-risk areas for storm floods. 3) The risks are mainly distributed in the city center, the lower reaches of the Huangpu River, the northern shore of Hangzhou Bay, the Qingpu-Songjiang depression in the southwest, and Chongming Island (southwest and northeast). Our work can provide decision-making basis for risk-sensitive based urban planning, flood risk adaptation, and resilience building in Shanghai. The methodology can also provide a reference for risk assessment in other coastal areas.

scholarly journals Mapping the morphology of sprawl and blight: A note on entropy

GeoScape ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 1-18
Author(s):  
Reza Banai ◽  
Anzhelika Antipova ◽  
Ehsan Momeni
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Expansion ◽  
Entropy Change ◽  
Metropolitan Region ◽  
List Type ◽  
Block Group ◽  
City Region ◽  
City Center ◽  
The City

Abstract The urban expansion from the city center to the suburb and beyond is indicated by Shannon entropy, a robust and versatile measure of sprawl. However, the metropolitan regionwide entropy masks the morphology of land cover and land use consequential to urban expansion within the city-region. To surmount the limitation, we focus on the block-group, which is a US census defined socio-spatial unit that identifies the metropolitan region’s development pattern structurally, forming tracts that comprise neighborhoods. The concentration and dispersion of land use and land cover by block-group reveals a North American metropolitan region’s commonly known but rarely measured spatial structure of its urban and suburban sprawl. We use parcel data from county assessor of property (GIS) and land cover pixel data from the National Land Cover Data (NLCD) to compute block-group land-use and land-cover entropy. The change in block group entropy over a decade indicates whether the city- region’s land use and land cover transition to a concentrated or dispersed pattern. Furthermore, we test a hypothesis that blight correlates with sprawl. Blight and sprawl are among the key factors that plague the metropolitan region. We determine the correlations with household income as well as (block group) distance from the city center. It turns out, blight is among the universally held distance-decay phenomena. The share of the block group’s blighted properties decays (nonlinearly) with distance from the city center. Highlights for public administration, management and planning: • The metropolitan region’s outward growth is highlighted by mapping the changing morphology of the block group within the city-region. • The block group entropy is computed with land use (parcel) and land cover (pixel) data. • The block group entropy change indicates the pattern of the land use and land cover transition with concentration or dispersion. • We test the hypothesis that blight correlates with sprawl with statistical models. • The block group’s blighted properties decrease (nonlinearly) with distance from the city center.

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