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Geohash: Integration of Disparate Geospatial Data
 

Geohash: Integration of Disparate Geospatial Data

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"Geohash: Integration of Disparate Geospatial Data," Abe Usher, Chief Innovation Officer, HumanGeo Group

"Geohash: Integration of Disparate Geospatial Data," Abe Usher, Chief Innovation Officer, HumanGeo Group

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    Geohash: Integration of Disparate Geospatial Data Geohash: Integration of Disparate Geospatial Data Presentation Transcript

    • Heatmaps & Geohash: Integration of Multi-Source Geospatial DataAbe Usher, CIOabe@thehumangeo.com703.955.1540@abeusher AKA Heatmaps are the HeatINFORMATION INTO INSIGHT
    • Our Menu of SubtopicsA LITTLE HISTORY WHY HEATMAPS? GEOHASH? DATA GONE WILD Big data requires new approaches. A new organizing construct forWhy geospatial data? information analysis. KITCHEN MODEL SPECIFIC EXAMPLES EASTER EGG New ways to combine internal data Concrete take-aways. Treats for making it through another with external new media for presentation. maximum insight. 2
    • What’s in it for you? 3
    • What’s in it for you? Make custom heatmaps Three iron laws of geospatial data integration and analysis 4
    • Whoami?People Data Beverages 5
    • GeohashGeohash is a coordinatetransformationthat facilitates combining twovariables (latitude and longitude)into a single(text) variablethat represents a bounding-boxcontaining the point of interest. 6
    • Heatmap“Discrete & continuous methods of kernel densityestimation” 7
    • A rose by any other name“Discrete & continuous methods of kernel densityestimation”  Gaussian  Quartic  Exponential  Triangular  Uniform  Epanechnikov 8
    • A rose by any other name“Discrete methods of kernel density estimation” 9
    • AboutBig Data (Digital) Human Geography  Predictive models of social drift & human behavior  Streaming media analytics  Micro-demographicsWe’re hiring! info@thehumangeo.com 10
    • Why Heatmaps and Geohash? Too much data 11
    • Why Heatmaps and Geohash? Too much data Trust in Internet data 12
    • Why Heatmaps and Geohash? Too much data Trust in Internet data Heatmaps look cool 13
    • Why Heatmaps and Geohash? Too much data Trust in Internet data Heatmaps look cool Geohash helps quantify data 14
    • Why Heatmaps and Geohash? Visual summary Too much data Trust in Internet data Heatmaps look cool Geohash helps quantify data Quantitative methods 15
    • Trust and Internet InformationTracy Morrow aka “Ice T” 16
    • Trust and Internet Information “Game knows game, baby.”Tracy Morrow aka “Ice T” 17
    • Trust and Internet Information “If you have expert knowledge, then you are capable of recognizing expert knowledge.” [paraphrased]Tracy Morrow aka “Ice T” 18
    • Trust and Internet InformationCan we actually trust this Internet stuff? 19
    • Trust and Internet Information 20
    • Trust and Internet Information 21
    • Trust and Internet Information 22
    • Salami Slicing Salami slicing: series of minor observations, resulting in a larger observation that would be difficult to performhttp://en.wikipedia.org/wiki/Salami_slicing 23
    • Seven Layer GLT1. OpenStreetMap data2. Flickr3. Panoramio4. Geonames.org5. Twitter6. Wikimapia7. 4Square* Geospatial Lattice of Trusted Data 24
    • Seven Layer GLT1. OpenStreetMap data2. Flickr3. Panoramio4. Geonames.org5. Twitter6. Wikimapia7. 4SquareSpatial Temporal User Finds From the Field (STUFFF) 25
    • Rule #1: Think in terms of aggregation Twitter data Panoramio Tourist photos Classified dataTwitter geohash ez420 – coffee shopPanoramio geohash ez420 – StarbucksClassified geohash ez420 - Wifi Trust through aggregation 26
    • Rule #1: Think in terms of aggregation Twitter dataTwitter geohash ez420 – coffee shopPanoramio geohash ez420 – StarbucksClassified geohash ez420 - Wifi Panoramio Tourist photosGeohash creates simple string variables. Classified dataMatching strings = super easyMatching similar coordinates = impossible Trust through aggregation 27
    • Rule #1: Think in terms of aggregation Twitter data Panoramio Tourist photosUse geohash to apply collaborativefiltering techniques to develop new Classified datamodels of trust & data confidence. Trust through aggregation 28
    • Rule #2: Selectively throw away precisionEntity #1 Latitude Longitude 40.998946 28.9232 41.005164 28.973668 41.018765 29.016412 41.062268 29.030145Entity #2 Latitude Longitude 40.999100 28.92111 41.018112 28.973991 41.018880 29.016902 41.062110 29.030122 29
    • Rule #2: Selectively throw away precisionEntity #1 Latitude Longitude Geohash 40.998946 28.9232 SXK94 41.005164 28.973668 SXK97 41.018765 29.016412 SXK9K 41.062268 29.030145 SXK9S 30
    • Rule #2: Selectively throw away precisionEntity #1 Latitude Longitude Geohash 40.998946 28.9232 SXK94 41.005164 28.973668 SXK97 41.018765 29.016412 SXK9K 41.062268 29.030145 SXK9SEntity #2 Latitude Longitude Geohash 40.999100 28.92111 SXK94 41.018112 28.973991 SXK97 41.018880 29.016902 SXK9K 41.062110 29.030122 SXK9S 31
    • Kitchen Model for Spatial Analysis 32
    • Kitchen Model for Spatial AnalysisChef 33
    • Kitchen Model for Spatial AnalysisChef Ingredients 34
    • Kitchen Model for Spatial AnalysisChef Ingredients Utensils 35
    • Kitchen Model for Spatial AnalysisChef Ingredients Utensils Presentation 36
    • Kitchen Model for Spatial AnalysisChef Ingredients Utensils Presentation 37
    • Types of HeatmapsTurnkey GeoCommons SpatialKey MapBox/TileMill ArcGIS Desktop QGISCustom Python R Javascript 38
    • Types of HeatmapsTurnkey GeoCommons SpatialKey MapBox/TileMill ArcGIS Desktop QGISCustom Python R Javascript 39
    • Heatmap: Recipe One“OSM Style”Get Python http://python.orgGet the sethoscope libraryhttp://www.sethoscope.net/heatmap/Get datahttp://bit.ly/geotweet_schttps://dev.twitter.com/docs/streaming-api/methods#locationsCommand line:heatmap.py -g portland.gpx -o output.png --height 800 --osm 40
    • 41
    • Heatmap: Recipe OneStitch it together in an MP4 movie!Get the CLI app:http://ffmpeg.org/Command line:heatmap.py -g portland.gpx -o output.png --height 800 –osm –affmpeg -i frame-%05d.png OSM_is_awesome.mp4 42
    • Heatmap: Recipe OneProps toSeth Golub from Googlehttp://www.sethoscope.net/ 43
    • Rule #3:Beware of population effects http://xkcd.com/1138/ 44
    • Rule #3: Beware of population effects Absolute valueNormalized value = Population estimate 45
    • Rule #3:Beware of population effects 34 72 52 22 46
    • Rule #3: Beware of population effects 34 Absolute value 2,000Normalized value = Population estimate 72 52 16,000 25,000 22 2,000 47
    • Conclusion1. Think in terms of aggregation2. Selectively throw away precision3. Beware of population effects 48
    • Contact UsHumanGeo NY HumanGeo DC1221 Avenue of the Americas 2500 Wilson BoulevardSuite 4200 | New York, NY 10020 Suite 310 | Arlington, VA 22201info@thehumangeo.com | 703.955.1540 | www.thehumangeo.com 49
    • Easter Egghttp://bit.ly/geotweet_sc 50