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

2020 ◽  
Author(s):  
Geoff Boeing

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.

Urban Science ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 28 ◽  
Author(s):  
Geoff Boeing

OpenStreetMap provides a valuable crowd-sourced database of raw geospatial data for constructing models of urban street networks for scientific analysis. This paper reports results from a research project that collected raw street network data from OpenStreetMap using the Python-based OSMnx software for every U.S. city and town, county, urbanized area, census tract, and Zillow-defined neighborhood. It constructed nonplanar directed multigraphs for each and analyzed their structural and morphological characteristics. The resulting data repository contains over 110,000 processed, cleaned street network graphs (which in turn comprise over 55 million nodes and over 137 million edges) at various scales—comprehensively covering the entire U.S.—archived as reusable open-source GraphML files, node/edge lists, and GIS shapefiles that can be immediately loaded and analyzed in standard tools such as ArcGIS, QGIS, NetworkX, graph-tool, igraph, or Gephi. The repository also contains measures of each network’s metric and topological characteristics common in urban design, transportation planning, civil engineering, and network science. No other such dataset exists. These data offer researchers and practitioners a new ability to quickly and easily conduct graph-theoretic circulation network analysis anywhere in the U.S. using standard, free, open-source tools.


2019 ◽  
Author(s):  
Geoff Boeing

OpenStreetMap provides a valuable crowd-sourced database of raw geospatial data for constructing models of urban street networks for scientific analysis. This paper reports results from a research project that collected raw street network data from OpenStreetMap using the Python-based OSMnx software for every U.S. city and town, county, urbanized area, census tract, and Zillow-defined neighborhood. It constructed nonplanar directed multigraphs for each and analyzed their structural and morphological characteristics. The resulting data repository contains over 110,000 processed, cleaned street network graphs (which in turn comprise over 55 million nodes and over 137 million edges) at various scales—comprehensively covering the entire U.S.—archived as reusable open-source GraphML files, node/edge lists, and GIS shapefiles that can be immediately loaded and analyzed in standard tools such as ArcGIS, QGIS, NetworkX, graph-tool, igraph, or Gephi. The repository also contains measures of each network’s metric and topological characteristics common in urban design, transportation planning, civil engineering, and network science. No other such dataset exists. These data offer researchers and practitioners a new ability to quickly and easily conduct graph-theoretic circulation network analysis anywhere in the U.S. using standard, free, open-source tools.


Author(s):  
Shinji Kobayashi ◽  
Luis Falcón ◽  
Hamish Fraser ◽  
Jørn Braa ◽  
Pamod Amarakoon ◽  
...  

Objectives: The emerging COVID-19 pandemic has caused one of the world’s worst health disasters compounded by social confusion with misinformation, the so-called “Infodemic”. In this paper, we discuss how open technology approaches - including data sharing, visualization, and tooling - can address the COVID-19 pandemic and infodemic. Methods: In response to the call for participation in the 2020 International Medical Informatics Association (IMIA) Yearbook theme issue on Medical Informatics and the Pandemic, the IMIA Open Source Working Group surveyed recent works related to the use of Free/Libre/Open Source Software (FLOSS) for this pandemic. Results: FLOSS health care projects including GNU Health, OpenMRS, DHIS2, and others, have responded from the early phase of this pandemic. Data related to COVID-19 have been published from health organizations all over the world. Civic Technology, and the collaborative work of FLOSS and open data groups were considered to support collective intelligence on approaches to managing the pandemic. Conclusion: FLOSS and open data have been effectively used to contribute to managing the COVID-19 pandemic, and open approaches to collaboration can improve trust in data.


2013 ◽  
Vol 74 (2) ◽  
pp. 195-207 ◽  
Author(s):  
Jingfeng Xia ◽  
Ying Liu

This paper uses Genome Expression Omnibus (GEO), a data repository in biomedical sciences, to examine the usage patterns of open data repositories. It attempts to identify the degree of recognition of data reuse value and understand how e-science has impacted a large-scale scholarship. By analyzing a list of 1,211 publications that cite GEO data to support their independent studies, it discovers that free data can support a wealth of high-quality investigations, that the rate of open data use keeps growing over the years, and that scholars in different countries show different rates of complying with data-sharing policies.


Metabolomics ◽  
2019 ◽  
Vol 15 (10) ◽  
Author(s):  
Kevin M. Mendez ◽  
Leighton Pritchard ◽  
Stacey N. Reinke ◽  
David I. Broadhurst

Abstract Background A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. Aim of Review To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. Key Scientific Concepts of Review This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.


Author(s):  
C. M. Ilie ◽  
M. A. Brovelli ◽  
S. Coetzee

<p><strong>Abstract.</strong> The 17 goals adopted by the United Nations (UN) are aimed at achieving a better and more sustainable future for all. For each goal, a set of indicators has been defined. The indicators measure progress towards achieving the respective SDG. For the majority of these indicators, geospatial information is needed to evaluate the current state of the indicator. While geospatial information is largely available in developed countries, this is not the case in many developing countries of the world. Furthermore, skills and capacity for calculating indicator values are also limited in many developing countries. To address these shortcomings, the third challenge of the 2018 UN OSGeo Committee Educational Challenges called for the development of training material for using open source software together with freely available high resolution global geospatial datasets in support of monitoring SDG progress. The resulting training material provides a step-by-step guide for calculating the state of SDG indicator 9.1.1, <i>Proportion of the rural population who live within 2km of an all-season road</i>, using open software and open data with global coverage. Through the development of this training material, we showed that anyone can monitor progress towards achieving SDG indicator 9.1.1 for their specific part of the world. Because open source software and open data were used, the indicator calculation is cost effective and completely sustainable.</p>


2019 ◽  
Vol 3 (2-2) ◽  
pp. 233
Author(s):  
Dasapta Erwin Irawan ◽  
Muhammad Aswan Syahputra ◽  
Prana Ugi ◽  
Deny Juanda Puradimaja

Hydrochemical analysis has emerged as a powerful methodology in geothermal system profiling. Indonesia is the capital of geothermal energy with its more than 100 active volcanoes. Therefore we need to have an analytical, data-driven, and user-focused online application of geothermal water quality. Proudly we introduce Thermostats (https://aswansyahputra.shinyapps.io/thermostats/). We collected water quality from 416 geothermal sites across Indonesia. Three main objectives are to provide an online open-free to use data repository, to visualize the dataset to suit user’s needs, and to help users understand the geothermal system of each particular site. At the end, we hope they like this system and donate their own dataset to make it better for future users. We designed this online app using Shiny, because it’s open source, lightweight and portable. It’s very intuitive to load our descriptive, bivariate and multivariate statistics. We selected Principal Component Analysis and Cluster Analysis as two strong statistics for water sample classification. Users could add their own dataset by making a pull request on Github (https://github.com/dasaptaerwin/thermostats) or sending it to us by email to make it visible in the application and included in the visualization. We make this application portable, so it can be installed on a local computer or a server, to enable an easy and fluid way of data sharing between collaborators.


2018 ◽  
Author(s):  
Geoff Boeing

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.


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