scholarly journals Street Network Models and Indicators for Every Urban Area in the World

2020 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Urban Area ◽  
Network Models ◽  
Street Network ◽  
The World

scholarly journals Street Network Models and Indicators for Every Urban Area in the World

2020 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Open Source ◽  
Urban Area ◽  
Open Data ◽  
Network Models ◽  
Data Repository ◽  
Street Network ◽  
Data Repositories ◽  
Urban Street ◽  
The World ◽  
Network Form

Cities worldwide exhibit a variety of street network patterns and configurations that shape human mobility, equity, health, and livelihoods. This study models and analyzes the street networks of each urban area in the world, using boundaries derived from the Global Human Settlement Layer. Street network data are acquired and modeled from OpenStreetMap with the open-source OSMnx software. In total, this study models over 160 million OpenStreetMap street network nodes and over 320 million edges across 8,914 urban areas in 178 countries, and attaches elevation and grade data. This article presents the study's reproducible computational workflow, introduces two new open data repositories of ready-to-use global street network models and calculated indicators, and discusses summary findings on street network form worldwide. It makes four contributions. First, it reports the methodological advances of this open-source workflow. Second, it produces an open data repository containing street network models for each urban area. Third, it analyzes these models to produce an open data repository containing street network form indicators for each urban area. No such global urban street network indicator dataset has previously existed. Fourth, it presents a summary analysis of urban street network form, reporting the first such worldwide results in the literature.

scholarly journals Urban Spatial Order: Street Network Orientation, Configuration, and Entropy

2018 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Network Models ◽  
Segment Length ◽  
Node Degree ◽  
Street Network ◽  
Spatial Order ◽  
Network Orientation ◽  
Orientation Order ◽  
The World ◽  
Unweighted Network ◽  
Study Sites

Street networks may be planned according to clear organizing principles or they may evolve organically through accretion, but their configurations and orientations help define a city’s spatial logic and order. Measures of entropy reveal a city’s streets’ order and disorder. Past studies have explored individual cases of orientation and entropy, but little is known about broader patterns and trends worldwide. This study examines street network orientation, configuration, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx. It measures the entropy of street bearings in weighted and unweighted network models, along with each city’s typical street segment length, average circuity, average node degree, and the network’s proportions of four-way intersections and dead-ends. It also develops a new indicator of orientation-order that quantifies how a city’s street network follows the geometric ordering logic of a single grid. A cluster analysis is performed to explore similarities and differences among these study sites in multiple dimensions. Significant statistical relationships exist between city orientation-order and other indicators of spatial order, including street circuity and measures of connectedness. On average, US/Canadian study sites are far more grid-like than those elsewhere, exhibiting less entropy and circuity. These indicators, taken in concert, help reveal the extent and nuance of the grid. These methods demonstrate automatic, scalable, reproducible tools to empirically measure and visualize city spatial order, illustrating complex urban transportation system patterns and configurations around the world.

scholarly journals Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rohit Kundu ◽  
Hritam Basak ◽  
Pawan Kumar Singh ◽  
Ali Ahmadian ◽  
Massimiliano Ferrara ◽  
...  
Keyword(s):  
Ct Scan ◽  
Detection System ◽  
Network Models ◽  
Test Cases ◽  
Gompertz Function ◽  
Neural Network Models ◽  
Source Codes ◽  
Ensemble Strategy ◽  
The World ◽  
Long Time

AbstractCOVID-19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.

scholarly journals HOW INDUSTRY 4.0 RESHAPES THE WORLD: RECOMMENDATIONS BASED ON COMPLEX GRAPH NETWORK ANALYSIS

2021 ◽  
Vol 1 ◽  
pp. 1755-1764
Author(s):  
Rongyan Zhou ◽  
Julie Stal-Le Cardinal
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Network Models ◽  
Global Supply Chain ◽  
Great Opportunity ◽  
Strategic Research ◽  
Complex Graph ◽  
The World ◽  
Tremendous Challenge

Abstract Industry 4.0 is a great opportunity and a tremendous challenge for every role of society. Our study combines complex network and qualitative methods to analyze the Industry 4.0 macroeconomic issues and global supply chain, which enriches the qualitative analysis and machine learning in macroscopic and strategic research. Unsupervised complex graph network models are used to explore how industry 4.0 reshapes the world. Based on the in-degree and out-degree of the weighted and unweighted edges of each node, combined with the grouping results based on unsupervised learning, our study shows that the cooperation groups of Industry 4.0 are different from the previous traditional alliances. Macroeconomics issues also are studied. Finally, strong cohesive groups and recommendations for businessmen and policymakers are proposed.

scholarly journals THE MONTHLY CHANGING OF THE LOWEST POPULATION DENGUE VIRUS INFECTION IN PATIENT AT SOERYA HOSPITAL SIDOARJO IN 00

2015 ◽  
Vol 2 (1) ◽  
pp. 20
Author(s):  
Soegeng Soegijanto
Keyword(s):  
Global Warming ◽  
Virus Infection ◽  
Dengue Virus ◽  
Urban Area ◽  
Dengue Shock Syndrome ◽  
Dengue Virus Infection ◽  
The World ◽  
Important Health ◽  
Good Environment ◽  
Growing Population

Background: Dengue virus infection is one of the important health problems in Indonesia, although the mortality rate has been decreased but many dengue shock syndrome cases is very difficult to be solving handled. To solve this problem, some factor that influence the prevalence of dengue virus infection should be studied. The Aim of Study: To detect some factor that maintain the higher case of dengue virus infection in patient at the Soerya Hospital Sepanjang, Sidoarjo. Material & Method: Study had been done at Soerya Hospital Sepanjang, Sidoarjo since January 1, 2007 until December 31, 2010. All cases suspected dengue virus infection in patient at soerya hospital were diagnosed based on WHO criteria in 1997 and PCR examination in ITD laboratory. The Result: In 2007, 2008, 2009, the monthly observation showed that decreasing cases of dengue virus infection in patient at Soerya Hospital had been found on September, but in 2010 this event had been found on November. Why this event to be change? It is suggested might be due to global warming in the world and the climate going to influence the environment sanitation. Interaction between agent host and environment becoming increase it might be due to the changing of climate can influence the growing population Aedes Aegyptie and Aedes Albopictus promoting to increase vector for transmit dengue virus infection. It is prominent in sub urban area, with have many peoples don’t aware with the bad environment sanitation. And many peoples showed very dynamic for living until the idea good environment do not be thought. By this condition the monthly population dengue virus infection in patient at hospital are going to maintain higher more than six months than usually. On the year 2007, 2008, 2009 the lowest cases found on September. In 2010, the lowest cases had been found on November. The Conclusion: Global warming, increasing sub urban area which have many peoples don’t aware with the bad environment sanitation and have highly dynamic peoples for getting some money for their life, could influence the higher cases dengue virus infection in patient at hospital more than 6 months.

scholarly journals Understanding Urban Growth in Beijing-Tianjin-Hebei Region over the Past 100 Years Using Old Maps and Landsat Data

2021 ◽  
Vol 13 (16) ◽  
pp. 3264 ◽  
Author(s):  
Shuang Li ◽  
Zhongqiu Sun ◽  
Yafei Wang ◽  
Yuxia Wang
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Spatial Data ◽  
Urban Expansion ◽  
Remote Sensing Images ◽  
The Past ◽  
Old Maps ◽  
The World ◽  
The City

Studying urban expansion from a longer-term perspective is of great significance to obtain an in-depth understanding of the process of urbanization. Remote sensing data are mostly selected to investigate the long-term expansion of cities. In this study, we selected the world-class urban agglomeration of Beijing-Tianjin-Hebei (BTH) as the study area, and then discussed how to make full use of multi-source, multi-category, and multi-temporal spatial data (old maps and remote sensing images) to study long-term urbanization. Through this study, we addressed three questions: (1) How much has the urban area in BTH expanded in the past 100 years? (2) How did the urban area expand in the past century? (3) What factors or important historical events have changed the development of cities with different functions? By comprehensively using urban spatial data, such as old maps and remote sensing images, geo-referencing them, and extracting built-up area information, a long-term series of urban built-up areas in the BTH region can be obtained. Results show the following: (1) There was clear evidence of dramatic urban expansion in this area, and the total built-up area had increased by 55.585 times, from 126.181 km2 to 7013.832 km2. (2) Continuous outward expansion has always been the main trend, while the compactness of the built-up land within the city is constantly decreasing and the complexity of the city boundary is increasing. (3) Cities in BTH were mostly formed through the construction of city walls during the Ming and Qing dynasties, and the expansion process was mostly highly related to important political events, traffic development, and other factors. In summary, the BTH area, similarly to China and most regions of the world, has experienced rapid urbanization and the history of such ancient cities should be further preserved with the combined use of old maps.

Artificial Neural Network Models for Large-Scale Data

2019 ◽  
pp. 406-439
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran
Keyword(s):  
Large Scale ◽  
Network Models ◽  
Data Sets ◽  
Neural Network Models ◽  
Large Scale Data ◽  
The World ◽  
Commercial Applications ◽  
Artificial Neural Network Models ◽  
Scale Data ◽  
Large Scale Data Sets

Artificial intelligence (ARTINT) and information have been famous fields for many years. A reason has been that many different areas have been promoted quickly based on the ARTINT and information, and they have created many significant values for many years. These crucial values have certainly been used more and more for many economies of the countries in the world, other sciences, companies, organizations, etc. Many massive corporations, big organizations, etc. have been established rapidly because these economies have been developed in the strongest way. Unsurprisingly, lots of information and large-scale data sets have been created clearly from these corporations, organizations, etc. This has been the major challenges for many commercial applications, studies, etc. to process and store them successfully. To handle this problem, many algorithms have been proposed for processing these big data sets.

scholarly journals Effectiveness of the Early Response to COVID-19: Data Analysis and Modelling

Systems ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 21 ◽  
Author(s):  
Edoardo Bertone ◽  
Martin Jason Luna Juncal ◽  
Rafaela Keiko Prado Umeno ◽  
Douglas Alves Peixoto ◽  
Khoi Nguyen ◽  
...  
Keyword(s):  
Network Models ◽  
High Volume ◽  
Early Response ◽  
Model Potential ◽  
Study Data ◽  
Community Response ◽  
Relative Importance ◽  
The World ◽  
Online Sources ◽  
Bayesian Network Models

Governments around the world have introduced a number of stringent policies to try to contain COVID-19 outbreaks, but the relative importance of such measures, in comparison to the community response to these restrictions, the amount of testing conducted, and the interconnections between them, is not well understood yet. In this study, data were collected from numerous online sources, pre-processed and analysed, and a number of Bayesian Network models were developed, in an attempt to unpack such complexity. Results show that early, high-volume testing was the most crucial factor in successfully monitoring and controlling the outbreaks; when testing was low, early government and community responses were found to be both critical in predicting how rapidly cases and deaths grew in the first weeks of the outbreak. Results also highlight that in countries with low early test numbers, the undiagnosed cases could have been up to five times higher than the officially diagnosed cases. The conducted analysis and developed models can be refined in the future with more data and variables, to understand/model potential second waves of contagions.

scholarly journals RENCANA PENATAAN AREA FASILITAS PENUNJANG YANG BERORIENTASI PADA AKTIVITAS BANDAR UDARA

2020 ◽  
Vol 1 (2) ◽  
pp. 2169
Author(s):  
Rudi Suhardi ◽  
Parino Rahardjo ◽  
Sylvie Wirawati
Keyword(s):  
Policy Analysis ◽  
Urban Area ◽  
Best Practice ◽  
Golf Courses ◽  
Open Spaces ◽  
Practice Location ◽  
The World ◽  
Support Area ◽  
The City ◽  
Oriented Approach

Soekarno-Hatta Airport is one of the Indonesian International airport located in the city of Tangerang with the top 20 busiest from 2010 to 2017 in the world, the area of facilities supporting the activities of Soekarno Hatta Airport which is set on Plan layout of Tangerang City area and plan Detail layout of urban area airport.The airport support area is an area that has facilities that directly and indirectly support airport activities, such as air aircraft workshop facilities, warehousing, sports, lodging, shops, restaurants, Golf courses, green open spaces, parking, leisure, and offices.But the Area that has been designated as a supporting facility does not work properly and maximally, so it needs to be rearranged to function properly.The authors use 5 methods of analysis, namely policy analysis, best practice, Location & site, market, and the need for space to produce a proposal to plan the arrangement of area facilities supporting Soekarno Hatta Airport with an Airport Oriented approach Activities.AbstrakBandara Soekarno Hatta adalah salah satu Bandara International Indonesia yang terletak di Kota Tangerang dengan peringkat 20 besar tersibuk sejak tahun 2010 hingga 2017 di dunia, area fasilitas penunjang aktivitas Bandara Soekarno Hatta yang ditetapkan pada Rencana Tata Ruang Wilayah Kota Tangerang dan Rencana Detail Tata Ruang Bagian Wilayah Perkotaan Bandara. Area fasilitas penunjang bandar udara adalah area yang memiliki fasilitas-fasilitas yang secara langsung dan tidak langsung menunjang kegiatan bandar udara, seperti fasilitas perbengkelan pesawat udara, pergudangan, olah raga, penginapan, toko, restoran, lapangan golf, ruang terbuka hijau, perparkiran, rekreasi, dan perkantoran. Namun Area yang telah ditetapkan sebagai fasilitas penunjang tidak berfungsi dengan seharusnya dan maksimal, sehingga perlu dilakukan penataan kembali agar bisa berfungsi dengan semestinya. Penulis menggunakan 5 metode analisis, yaitu analisis kebijakan, best practice, lokasi & tapak, pasar, dan kebutuhan ruang untuk menghasilkan usulan rencana penataan area fasilitas penunjang Bandara Soekarno Hatta dengan pendekatan Airport Oriented Activities.

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