hi Rob thanks for these excellent series
best wishes for '2012' (TM) Andreas Sent from my eXtended BodY On 31 dec. 2011, at 14:06, Rob Myers <[email protected]> wrote: > Blog post with picture, links and better formatting: > > http://robmyers.org/2011/12/31/psychogeodata-33/ > > The examples of Psychogeodata given so far have used properties of the > geodata graph and of street names to guide generation of Dérive. There > are many more ways that Psychogeodata can be processed, some as simple > as those already discussed, some much more complex. > General Strategies > > There are some general strategies that most of the following techniques > can be used as part of. > > Joining the two highest or lowest examples of a particular measure. > > Joining the longest run of the highest or lowest examples of a > particular measure. > > Joining a series of destination waypoints chosen using a particular > measure. > > The paths constructed using these strategies can also be forced to be > non-intersecting, and/or the waypoints re-ordered to find the shortest > journey between them. > Mathematics > > Other mathematical properties of graphs can produce interesting walks. > The length of edges or ways can be used to find sequences of long or > short distances. > > Machine learning techniques, such as clustering, can arrange nodes > spatially or semantically. > > Simple left/right choices and fixed or varying degrees can create > zig-zag or spiral paths for set distances or until the path self-intersects. > Map Properties > > Find long or short street names or street names with the most or fewest > words or syllables and find runs of them or use them as waypoints. > > Find all the street names on a particular theme (colours, saints' names, > trees) and use them as waypoints to be joined in a walk. > > Streets that are particularly straight or crooked can be joined to > create rough or smooth paths to follow. > > If height information can be added to the geodata graph, node elevation > can be used as a property for routing. Join high and low points, flow > downhill like water, or find the longest runs of valleys or ridges. > > Information about Named entities extracted from street, location and > district names from services such as DBPedia or Freebase and used to > connect them. Dates, historical locations, historical facts, > biographical or scientific information and other properties are > available from such services in a machine-readable form. > > Routing between peaks and troughs in sociological information such as > population, demographics, crime occurrence, ploitical affiliation, > property prices can produce a journey through the social landscape. > Locations of Interest > > Points of interest in OpenStreetMap's data are represented by nodes > tagged as "historic", "amenity", "leisure", etc. . It is trivial to find > these nodes to use as destinations for walks across the geodata graph. > They can then be grouped and used as waypoints in a route that will > visit every coffee shop in a town, or one of each kind of amenity in > alphabetical order, in an open or closed path for example. Making a > journey joining each location with a central base will produce a star shape. > > Places of worship (or former Woolworth stores can be used to find > https://en.wikipedia.org/wiki/Ley_line using linear regression or the > techniques discussed below in "Geometry and Computer Graphics". > Semantics > > The words of poems or song lyrics (less stopwords), matched either > directly or through hypernyms using Wordnet, can be searched for in > street and location names to use as waypoints in a path. Likewise named > entities extracted from stories, news items and historical accounts. > > More abstract narratives can be constructed using concepts from The > Hero's Journey. > > Nodes found using any other technique can be grouped or sequenced > semantically as waypoints using Wordnet hypernym matching. > Isomorphism > > Renamed Tube maps, and journeys through one city navigated using a map > of another, are examples of using isomorphism in Psychogeography. > > Entire city graphs are very unlikely to be isomorphic, and the routes > between famous locations will contain only a few streets anyway, so > sub-graphs are both easier and more useful for matching. Better > geographic correlations between locations can be made by scoring > possible matches using the lengths of ways and the angles of junctions. > Match accuracy can be varied by changing the tolerances used when scoring. > > Simple isomorphism checking can be performed using The NetworkX > library's functions . Projecting points from a subgraph onto a target > graph then brute-force searching for matches by varying the matrix used > in the projection and scoring each attempt based on how closely the > points match . Or Isomorphisms can be bred using genetic algorithms, > using degree of isomorphism as the fitness function and proposed > subgraphs as the population. > The Social Graph > > Another key contemporary application of graph theory is Social Network > Analysis. The techniques and tools from both the social science and web > 2.0 can be applied directly to geodata graphs. > > Or the graphs of people's social relationships from Facebook, Twitter > and other services can mapped onto their local geodata graph using the > techniques from "Isomorphism" above, projecting their social space onto > their geographic space for them to explore and experience anew. > Geometry and Computer Graphics > > Computer geometry and computer graphics or computer vision techniques > can be used on the nodes and edges of geodata to find forms. > > Shapes can be matched by using them to cull nodes using an insideness > test or to find the nearest points to the lines of the shape. Or > line/edge intersection can be used. Such matching can be made fuzzy or > accurate using the matching techniques in "Isomorphism". > > Simple geometric forms can be found - triangles, squares and > quadrilaterals, stars. Cycle bases may be a good source of these. Simple > shapes can be found - smiley faces, house shapes, arrows, magical > symbols. Sequences of such forms can be joined based on their > mathematical properties or on semantics. > > For more complex forms, face recognition, object recognition, or OCR > algorithms can be used on nodes or edges to find shapes and sequences of > shapes. > > Classic computer graphics methods such as L-sytems, turtle graphics, > Conway's Game of Life, or Voronoi diagrams can be applied to the Geodata > graph in order to produce paths to follow. > > Geometric animations or tweens created on or mapped onto the geodata > graph can be walked on successive days. > Lived Experience > > GPS traces generated by an individual or group can be used to create new > journeys relating to personal or shared history and experience. So can > individual or shared checkins from social networking services. Passenger > level information for mass transport services is the equivalent for > stations or airports. > > Data streams of personal behaviour such as scrobbles, purchase > histories, and tweets can be fetched and processed semantically in order > to map them onto geodata. This overlaps with "Isomorphism", "Semantics", > and "The Social Graph" above. > Conclusion > > This series of posts has made the case for the concept, practicality, > and future potential of Psychogeodata. The existing code produces > interesting results, and there's much more that can be added and > experienced. > > (Part one of this series can be found here, part two can be found here . > The source code for the Psychogeodata library can be found here .) > _______________________________________________ > NetBehaviour mailing list > [email protected] > http://www.netbehaviour.org/mailman/listinfo/netbehaviour > _______________________________________________ NetBehaviour mailing list [email protected] http://www.netbehaviour.org/mailman/listinfo/netbehaviour
