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<!DOCTYPE html>
<html lang="en-us">
<head>
<meta charset="UTF-8">
<title>India Analysis</title>
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<link href='https://fonts.googleapis.com/css?family=Open+Sans:400,700' rel='stylesheet' type='text/css'>
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['Year', 'Bengaluru', 'Delhi', 'Mumbai'],
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data.addColumn('string', 'POI Type');
data.addColumn('number', '2007');
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</head>
<body>
<section class="page-header">
<h1 class="project-name">India-data-analysis</h1>
<h2 class="project-tagline"></h2>
<a href="https://github.com/IndyHurt/india-data-analysis" class="btn">View on GitHub</a>
<a href="https://github.com/IndyHurt/india-data-analysis/zipball/master" class="btn">Download data</a>
<a href="https://github.com/IndyHurt/india-data-analysis/blob/gh-pages/queries/osm-analysis.sql" class="btn">View queries</a>
</section>
<section class="main-content">
<h3>
<a id="welcome-to-github-pages" class="anchor" href="#welcome-to-github-pages" aria-hidden="true"><span class="octicon octicon-link"></span></a>Data Quality 101</h3>
<p> When measuring data quality, what exactly does that mean? One may try to formulate quantitative measures that identify a data set as superior by comparison, complete, accurate, consistent, and current. Additional measurement may focus on documentation, and source evaluation with respect to the reputation or authoritative nature of the data provider. </p>
<p> The original purpose of data collection and the original scale at which it was collected may also have bearing on the utility of the data for purposes beyond it's original intent. With this in mind, data quality may be measured with specific use cases in mind. For spatial data, a few examples include routing, search, and cartographic rendering. </p>
<h3>
<a id="designer-templates" class="anchor" href="#designer-templates" aria-hidden="true"><span class="octicon octicon-link"></span></a>Previous Methods Used to Evaluate OpenStreetMap data quality</h3>
<p>Techniques to assess data quality of OpenStreetMap data have been conducted by several researchers in recent years. Analysis of the road network is common, but others have focused on other features like buildings [<a href="#r1">1</a>]. While several [2-6] measure characteristics of OpenStreetMap data against authoritative sources, others take a community approach by evaluating the community of editors [7-9].</p>
<p>Complementing the various OpenStreetMap research efforts that have been published in peer reviewed journals, code repositories featuring OpenStreetMap data analysis tools are available from a number of contributors. In this space, find iOSMAnalyzer: a python tool to generate data quality reports utilizing Osmium, OSM-History-Splitter, OSM-History-Render/Importer, and MatplotLib. Find also nearly two dozen OpenStreetMap data quality checking Perl scripts written by Gerhard Gary68</p>
<h3>
<a id="creating-pages-manually" class="anchor" href="#creating-pages-manually" aria-hidden="true"><span class="octicon octicon-link"></span></a>Community and Quality</h3>
<p>The relationship between community and quality is fascinating. OpenStreetMap data quality clearly benefits from an extensive community of editors. It is easy to imagine why this might be the case. More contributors leads to more content, and faster corrections when errors arise, including vandalism [10].</p>
<h3>
<a id="authors-and-contributors" class="anchor" href="#authors-and-contributors" aria-hidden="true"><span class="octicon octicon-link"></span></a>Characteristics of Bengaluru, Delhi, and Mumbai</h3>
<p>What is happening in the world of OpenStreetMap edits for India? Here is a first look at Bengaluru, Delhi, and Mumbai. Starting with the road network, Bengaluru and Delhi have been increasing over time. Mumbai has been relatively stable and could use some encouragement, but digging a little deeper reveals some interesting insights.</p>
<div id="curve_chart" style="width: 900px; height: 500px"></div>
<p align=center style="font-size:14px; background-color:#f0ebeb;">See <a href="https://github.com/IndyHurt/india-data-analysis/blob/gh-pages/queries/osm-analysis.sql#L1-L5">example query</a> for this chart.</p>
<p>While coverage, or in this case the accumulation of kilometers is important, additional attributes can reveal valuable details about the type of editing taking place in these areas over the last 8 years. Surveying the data for evidence of local knowledge often yields fuzzy results, but additional attributes and enhancement to features can still be extracted. Many types of enhancements would not easily be possible for editors relying solely on aerial imagery. Two examples below include road features with names and road features with oneway designations. In some parts of the world, the line type visible in aerial imagery helps identify a oneway road, but this isn't a global standard, and this technique of identifying road type requires relatively high resolution aerial imagery. With respect to names, this is where Mumbai shines. The total accumulated kilometers of road may be small in comparison to Bengaluru and Delhi, but the percentage of Mumbai roads with names is quite high.</p>
<p>Should it be 100%? Actually, no, not in many dense urban areas in India. There are many areas that rely more heavily on place names associated with residential sectors. Of course that means it is very important to have them.</p>
<div id="curve_chart2" style="width: 900px; height: 500px"></div>
<p align=center style="font-size:14px; background-color:#f0ebeb;">See <a href="https://github.com/IndyHurt/india-data-analysis/blob/gh-pages/queries/osm-analysis.sql#L7-L11">example query</a> for this chart.</p>
<div id="curve_chartOneways" style="width: 900px; height: 500px"></div>
<p align=center style="font-size:14px; background-color:#f0ebeb;">See <a href="https://github.com/IndyHurt/india-data-analysis/blob/gh-pages/queries/osm-analysis.sql#L21-L25">example query</a> for this chart.</p>
<p>There is an interesting relationship between kilometers of road edited and the number of segments edited. In years 2008 through 2014, the number of edits to existing features outpaces the addition of brand new features in nearly every year for Bengaluru and Delhi. Of course, Mumbai is doing it's own thing and needs further investigation.</p>
<p>With the number of segments rising faster than the number of kilometers in Bengaluru and Delhi, we are seeing a trend towards smaller segments or segmentation of larger features. This makes sense.</p>
<p>When editors are faced with a blank canvas, the long easy highways are the low hanging fruit. A shift towards smaller segments over time likely indicates an increase in detail, and this is good! A community of editors is taking the time to fill in the details, and they are in for the long haul.</p>
<p>The graphics below show stacked bar charts for the kilometer of edits to new and exisiting roads each year overlaid with dotted line charts representing the number of road segments edited each year. New verses existing is an approximation derived from version numbers. Same data, two views, and each year is an accumulation of prior years.</p>
<div id="chart_divbre" style="width: 900px; height: 500px;"></div>
<div id="chart_divdrs" style="width: 900px; height: 500px;"></div>
<div id="chart_divmrs" style="width: 900px; height: 500px;"></div>
<p align=center style="font-size:14px; background-color:#f0ebeb;">See <a href="https://github.com/IndyHurt/india-data-analysis/blob/gh-pages/queries/osm-analysis.sql#L13-L19">example query</a> for the segments added to this chart.</p>
<div id="chart_div" style="width: 900px; height: 500px;"></div>
<div id="chart_div2" style="width: 900px; height: 500px;"></div>
<div id="chart_div3" style="width: 900px; height: 500px;"></div>
<p align=center style="font-size:14px; background-color:#f0ebeb;">See <a href="https://github.com/IndyHurt/india-data-analysis/blob/gh-pages/queries/osm-analysis.sql#L140-L155">example query</a> for this chart.</p>
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<a id="authors-and-contributors" class="anchor" href="#authors-and-contributors" aria-hidden="true"><span class="octicon octicon-link"></span></a>Next steps</h3>
<p>Writing goes here...</p>
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<a id="support-or-contact" class="anchor" href="#support-or-contact" aria-hidden="true"><span class="octicon octicon-link"></span></a>References</h3>
<p>
<ol>
<li><a id="r1"></a>Fan, H., et al., Quality assessment for building footprints data on OpenStreetMap. International Journal of Geographical Information Science, 2014. 28(4): p. 700-719.</li>
<li>Kounadi, O., Assessing the quality of OpenStreetMap data, in Department of Civic, Environmental And Geomatic Engineering. 2009, University College of London. p. 80.</li>
<li>Haklay, M., How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets. Environment and Planning B: Planning and Design, 2010. 37(4): p. 682-703.</li>
<li>Ather, A., A Quality Analysis of OpenStreetMap Data, in Department of Civil, Environmental & Geomatic Engineering. 2009, University College London. p. 81.</li>
<li>Haklay, M., et al., How Many Volunteers Does it Take to Map an Area Well? The Validity of Linus’ Law to Volunteered Geographic Information. The Cartographic Journal, 2010. 47(4): p. 315-322.</li>
<li>Girres, J.-F. and G. Touya, Quality Assessment of the French OpenStreetMap Dataset. Transactions in GIS, 2010. 14(4): p. 435-459.</li>
<li>Neis, P. and A. Zipf, Analyzing the Contributor Activity of a Volunteered Geographic Information Project — The Case of OpenStreetMap. ISPRS International Journal of Geo-Information, 2012. 1(2): p. 146.</li>
<li>Bégin, D., R. Devillers, and S. Roche, Assessing Volunteered Geographic Information (VGI) Quality Based on Contributors' Mapping Behaviours. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013. XL-2/W1: p. 6.</li>
<li>Mooney, P. and P. Corcoran, Characteristics of Heavily Edited Objects in OpenStreetMap. Future Internet, 2012. 4(1): p. 285.</li>
<li>Neis, P., M. Goetz, and A. Zipf, Towards Automatic Vandalism Detection in OpenStreetMap. ISPRS International Journal of Geo-Information, 2012. 1(3): p. 315.</li>
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