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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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  </channel><item rdf:about="http://arxiv.org/abs/1411.0722">
    <title>[1411.0722] Visualizing the &quot;Heartbeat&quot; of a City with Tweets</title>
    <dc:date>2014-11-14T23:11:07+00:00</dc:date>
    <link>http://arxiv.org/abs/1411.0722</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Describing the dynamics of a city is a crucial step to both understanding the human activity in urban environments and to planning and designing cities accordingly. Here we describe the collective dynamics of New York City and surrounding areas as seen through the lens of Twitter usage. In particular, we observe and quantify the patterns that emerge naturally from the hourly activities in different areas of New York City, and discuss how they can be used to understand the urban areas. Using a dataset that includes more than 6 million geolocated Twitter messages we construct a movie of the geographic density of tweets. We observe the diurnal "heartbeat" of the NYC area. The largest scale dynamics are the waking and sleeping cycle and commuting from residential communities to office areas in Manhattan. Hourly dynamics reflect the interplay of commuting, work and leisure, including whether people are preoccupied with other activities or actively using Twitter. Differences between weekday and weekend dynamics point to changes in when people wake and sleep, and engage in social activities. We show that by measuring the average distances to the heart of the city one can quantify the weekly differences and the shift in behavior during weekends. We also identify locations and times of high Twitter activity that occur because of specific activities. These include early morning high levels of traffic as people arrive and wait at air transportation hubs, and on Sunday at the Meadowlands Sports Complex and Statue of Liberty. We analyze the role of particular individuals where they have large impacts on overall Twitter activity. Our analysis points to the opportunity to develop insight into both geographic social dynamics and attention through social media analysis.
]]></description>
<dc:subject>social-media collective-behavior Twitter time-series visualization GIS social-dynamics rather-interesting complexology urban-landscapes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0c35f5038942/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1304.1296">
    <title>[1304.1296] Happiness and the Patterns of Life: A Study of Geolocated Tweets</title>
    <dc:date>2013-09-15T12:57:04+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.1296</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The patterns of life exhibited by large populations have been described and modeled both as a basic science exercise and for a range of applied goals such as reducing automotive congestion, improving disaster response, and even predicting the location of individuals. However, these studies previously had limited access to conversation content, rendering changes in expression as a function of movement invisible. In addition, they typically use the communication between a mobile phone and its nearest antenna tower to infer position, limiting the spatial resolution of the data to the geographical region serviced by each cellphone tower. We use a collection of 37 million geolocated tweets to characterize the movement patterns of 180,000 individuals, taking advantage of several orders of magnitude of increased spatial accuracy relative to previous work. Employing the recently developed sentiment analysis instrument known as the 'hedonometer', we characterize changes in word usage as a function of movement, and find that expressed happiness increases logarithmically with distance from an individual's average location.
]]></description>
<dc:subject>sociology social-media GIS sentiment-analysis feature-extraction visualization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b833dcc6693e/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1307.2203">
    <title>[1307.2203] Self-organization versus top-down planning in the evolution of a city</title>
    <dc:date>2013-07-22T14:08:01+00:00</dc:date>
    <link>http://arxiv.org/abs/1307.2203</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Interventions of central, top-down planning are serious limitations to the possibility of modelling the dynamics of cities. An example is the city of Paris (France), which during the 19th century experienced large modifications supervised by a central authority, the `Haussmann period'. In this article, we report an empirical analysis of more than 200 years (1789-2010) of the evolution of the street network of Paris. We show that the usual network measures display a smooth behavior and that the most important quantitative signatures of central planning is the spatial reorganization of centrality and the modification of the block shape distribution. Such effects can only be obtained by structural modifications at a large-scale level, with the creation of new roads not constrained by the existing geometry. The evolution of a city thus seems to result from the superimposition of continuous, local growth processes and punctual changes operating at large spatial scales.
]]></description>
<dc:subject>self-organization city-planning GIS geometry graph-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3d684c9af2c3/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1206.0217">
    <title>[1206.0217] Efficient techniques for mining spatial databases</title>
    <dc:date>2012-06-09T10:36:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.0217</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Clustering is one of the major tasks in data mining. In the last few years, Clustering of spatial data has received a lot of research attention. Spatial databases are components of many advanced information systems like geographic information systems VLSI design systems. In this thesis, we introduce several efficient algorithms for clustering spatial data. First, we present a grid-based clustering algorithm that has several advantages and comparable performance to the well known efficient clustering algorithm. The algorithm has several advantages. The algorithm does not require many input parameters. It requires only three parameters, the number of the points in the data space, the number of the cells in the grid and a percentage. The number of the cells in the grid reflects the accuracy that should be achieved by the algorithm. The algorithm is capable of discovering clusters of arbitrary shapes. The computational complexity of the algorithm is comparable to the complexity of the most efficient clustering algorithm. The algorithm has been implemented and tested against different ranges of database sizes. The performance results show that the running time of the algorithm is superior to the most well known algorithms (CLARANS [23]). The results show also that the performance of the algorithm do not degrade as the number of the data points increases."]]></description>
<dc:subject>GIS statistics clustering context-sensitive-data nudge-targets data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5baf6ef23848/</dc:identifier>
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<item rdf:about="http://www.berfrois.com/2012/04/maps-governments-cannot-ignore/">
    <title>‘The aim is to produce maps that governments cannot ignore’ | berfrois</title>
    <dc:date>2012-04-06T11:35:24+00:00</dc:date>
    <link>http://www.berfrois.com/2012/04/maps-governments-cannot-ignore/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Consider events in the Democratic Republic of the Congo, formerly Zaire. There, in the aftermath of a long civil war, the government is currently zoning its forests — which cover as much as 316 million acres, an area nearly the size of France, Germany and Spain combined — in preparation for their mass allocation to logging companies. Old European timber conglomerates want to reactivate their concessions, some dating back almost to the brutal days more than a century ago when the entire country was run by King Leopold of Belgium. Logging newcomers from Malaysia and China also want a slice of the action."]]></description>
<dc:subject>GIS mapping corporatism activism ontological-war</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f5bd478626b5/</dc:identifier>
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<item rdf:about="http://petewarden.typepad.com/searchbrowser/2010/02/how-to-split-up-the-us.html">
    <title>PeteSearch: How to split up the US</title>
    <dc:date>2010-02-10T14:35:04+00:00</dc:date>
    <link>http://petewarden.typepad.com/searchbrowser/2010/02/how-to-split-up-the-us.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Stretching from New York to Minnesota, this belt's defining feature is how near most people are to their friends, implying they don't move far. In most cases outside the largest cities, the most common connections are with immediately neighboring cities, and even New York only has one really long-range link in its top 10. Apart from Los Angeles, all of its strong ties are comparatively local."
]]></description>
<dc:subject>social-networks cultural-norms sociology American-cultural-assumptions Facebook geography network-culture visualization GIS</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6ecaf1e8899e/</dc:identifier>
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<item rdf:about="http://annarborchronicle.com/2009/07/01/city-and-residents-to-make-tree-policy/">
    <title>The Ann Arbor Chronicle » City and Residents to Make Tree Policy</title>
    <dc:date>2009-07-02T12:25:06+00:00</dc:date>
    <link>http://annarborchronicle.com/2009/07/01/city-and-residents-to-make-tree-policy/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We asked the city of Ann Arbor for all the electronic deliverables from Davey. And we provide the following data with a caveat: On Monday evening, city staff stressed that they were still doing some quality control work on the initial data set – so the data provided to The Chronicle is a snapshot of the city’s trees as assessed by the Davey Resource Group. The city’s inventory will presumably be maintained as a frequently updated data set that changes as trees are pruned, removed, or planted."
]]></description>
<dc:subject>local Ann-Arbor GIS raw-data-now trees dataset mapping transparency open-access public-policy</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bc60a96ac1bd/</dc:identifier>
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