Joining Space and Time in Geographic Features

I’d like to make a case for extending the GeoJSON standard for representing geographic features, to better account for their temporal properties. Of course, JSON-LD should play a part, and I would add elements of the Topotime Elijah Meeks and I have been working on. A bit about my rationale follows, but first a tentative template for discussion (an image here due to WordPress theme; on github):geojson_plus-topotime

SPACE (PLACE) + TIME (PERIOD) = SETTING

Neolithic Anatolia and Iron Age Britain are equally historical periods and geographic features, as each has both spatial and temporal extents. Depending on the circumstance and computational requirements, one perspective may be more meaningful than the other, but the answer as to whether they are places or periods is…yes. More generally, places of all kinds (natural features and man-made artifacts) and periods (including events in this discussion) can be considered dynamic geographic phenomena, defined as entities associated with particular locations on or near the earth surface, whose attributes over time are integral to their analysis. We don’t always care about the temporality of places or the spatiality of periods, but the way these qualities are bound together should be an important consideration in geographic and historical databases and the software publications they support.

To represent a place on a map, or to reason about its location amongst other places, we need a description of its spatial extents; its temporal extents are optional. Likewise, to represent a period/event on a timeline, or to reason about its location and duration amongst other periods, we need a description of its temporal extents; its spatial extents are optional.

GeoJSON is a popular and stable standard for representing collections of geographic features in terms of their location geometries and any number of additional properties. Although the properties of geographic features are typically dynamic, meaning that their digital representations should indicate a timespan of validity, the GeoJSON standard does not attempt to handle that explicitly. If your features’ temporality matters, you can add properties—for example, some intermittent medieval phenomenon might have:

“ValidTimespans”: [{“1213-03-04”,“1213-04-10”}, {“1214-01-02”,“1214-04-01”}]

Or, you might record Simile Timeline formatted properties like these:

“start”:"May 01 1963 00:00:00 GMT-0600",
“latestStart”: “May 01 1963 06:00:00 GMT-0600”,
"earliestEnd”: “May 31 1963 18:00:00 GMT-0600”,
“end”:"Sat Jun 01 1963 00:00:00 GMT-0600"

What computational events these statements inform or trigger is entirely on you the developer.  Mapping libraries like Leaflet and D3 understand the geometries of GeoJSON, but do nothing with other properties. A timeline library like Simile understands collections of events described with its standard properties, but can do nothing (natively) about placing them on a map.

Does it make sense to develop a standard way of combining collections of features and collections of periods (and events)? A standard data format, parts of which can be read by mapping libraries, parts by timeline libraries, and ultimately all by “timemap” libraries? It seems to me yes, and my first stab at what it might look like is in the above record of a figurine excavated at Çatalhöyük. A few hundred thousand more to follow once I/we sort this out.

I’ve recently worked on a simple ontology design pattern for a Setting, with Krzysztof (Jano) Janowicz of UC Santa Barbara and Carsten Keßler of Hunter College, which follows on from the geosetting in Mike Worboys’ and Kathleen Stewart’s GEM Model (2004). A Setting joins the spatial and temporal contexts for things and events. Sadly, due to the idiotic nature of the academic publishing paradigm (okay, maybe just outdated), I’m not at liberty to share that “in press, accepted” work directly yet, but I can share my attempts to engineer real things it underlies conceptually. Jano will be leading some efforts to fully specify the Setting pattern at the upcoming GeoVoCamp2014 in Santa Barbara (14-16 March). Everything coming out of that will be freely available.

Cited

Worboys, M. & Hornsby, K. (2004). From objects to events: GEM, the geospatial event model. In Egenhofer, M. J., C. Freksa, H. J. Miller (Eds.) Geographic Information Science: Third International Conference, LNCS3234: 327–344.

Posted in computing place, time | Leave a comment

Topotime and place

two-up_blogI’ve recently been co-developing with colleague Elijah Meeks something called Topotime, which at this stage is experimental software for rendering timelines and doing some computational reasoning about historical timespans, such as calculating overlap. The first adjective we use to describe this work is pragmatic, because we felt we had thought hard enough about time versus temporality for digital humanities work [1], and built enough temporal data models and timelines, that we should begin some concrete steps to “operationalize” [2] our views and personal wishlists in some working software. The results to date have just been publicly released on GitHub, and we hope other will participate in its further development. Elements of the Topotime data model and software are novel (we think) but it is built around a couple of common and successful design patterns.

 First, Topotime models Periods in PeriodCollections, much as GeoJSON models Features in FeatureCollections. GeoJSON Features have a typed geometry and unlimited number of user-defined properties. Topotime Periods have typed timespans (tSpan) and unlimited user-defined properties. Topotime can be written as a JSON object, just as GeoJSON is. I find the symmetry between representation requirements for spatial things and temporal things astonishing, although it would probably not surprise physicists. For starters, both have names, metrical representations (geometries, even), and are usefully typed. The close relationship between places and periods will be a refrain on this blog.

features_periods-compare

The second borrowed pattern is representing the uncertain boundaries of intervals as intervals themselves, not “instants” (there aren’t very many instants in historiography). The result is a quad of start (s), latest start (ls), earliest end (ee), and end (e). The first and third of these can be stated in natural language as “not before,” and the second and fourth as “not after.” This pattern appears in Simile Timeline and in several scholarly works I cited in an earlier blog post.

temporal-geometry_fig4Topotime extends that pattern to allow any of these to be qualified as “about” or “approximately” (~) some day, month or year. It also parses an elaboration of the starting and ending spans (sls and eee respectively). The result is a function returning a probability y for any time x. The area under the function’s curve, although not a useful number in and of itself, can be used to good effect in computing overlap with other period or event timespans, and with query areas (as discussed in this short paper [PDF], and earlier demonstrated by Kauppinen et al [3]). The Topotime model also permits specifying intermittent, multi-part timespans which can be cyclical or irregular.

Meeting of minds (and conceptualizations)

Topotime’s name, courtesy of Elijah, stems from our wish to capture certain topological relations between periods (their timespans actually). We can know a period or event began after another and not know when that is exactly but wish to represent and reason about that adjacency. Similarly, we may know two events (lives, e.g.) overlapped, but have only minimal information about their starts and ends.

As it turned out, tackling that issue led to a more involved data model. Its hard to know where to put the bounds on development projects, due to the EAGER principle we live by here: Everything’s A Graph and Everything’s Related. Both Elijah and I have been working at event data models for multiple projects for several years, and this was an opportunity to operationalize some of our individual perspectives, which differ but seem to have important overlaps as well.

These are a couple of the agreements and how they’ve appeared in Topotime so far:

  • There are temporal things, which include events, historical periods, and lifespans of things, people and groups (e.g. nations). They all share some representation requirements, so in software we can make a super-class for them, potentially specializing distinctive differences in sub-classes later. But for the time being every temporal thing is a Period, for lack of a better all-encompassing term, and we don’t do anything different for events, lifespans and historical periods. If you add an attribute like class or css_class to the generic periods you can make them render distinctively in a timeline app.
  • Periods have meaningful relationships to other Periods, some of which are non-topological. For this, Topotime recognizes a relations[ ] array of simple subject-predicate-object triples. This will be written as JSON-LD soon, and therefore be Semantic Web compatible. That is, although relationships between the timespans of two events are metrical, measured, and possibly incidental (they overlap, abut, are disjoint, etc.), relationships between periods are a different thing. The most basic is compositional, or mereological ( Peter Simons’ Parts: A study in ontology is fascinating, and short). Events are composed of or contained by other events. We use a part_of relation for this.

Other relationships researchers might wish to encode include caused, required, led_to, etc., none of which we deal with yet. At minimum we might like to visualize our understandings and arguments about these in timeline interfaces (perhaps along the lines of Nowiskie and Drucker’s PlaySpace 2003 [1]). Quite possibly, we can find further interesting ways to compute over them, but they first must find their way into data models.


[1] Although not as hard Bethany Nowiskie and Johanna Drucker! I only recently came across a trove of their interesting work theorizing time v. temporality, and building out pilots for novel timeline applications for digital humanities. For example, the Temporal Modelling Project and PlaySpace 2003 [screenshots]

[2] A term with plenty of history, but recently the subject of a really nice Stanford Lit Lab pamphlet by Franco Moretti.

[3] Tomi Kauppinen, Glauco Mantegari, Panu Paakkarinen, Heini Kuittinen, Eero Hyvönen, and Stefania Bandini. (2010). Determining Relevance of Imprecise Temporal Intervals for Cultural Heritage Information Retrieval. International Journal of Human-Computer Studies, Volume 68, Issue 9, pp. 549-560 , Elsevier. Preprint PDF

Posted in computing place, time | Tagged , | Leave a comment

Geography at Stanford, part I

Yesterday I listened to an excellent lecture by anthropologist Sidney Mintz (wiki; his site) on sugar, plantations, and world history. The occasion was a half-day panel titled “Caribbean Connections: Plantation Landscapes and Global History.” Mintz’s lifelong focus has been on the Caribbean, but he touched on other regions in this talk. His interests and writings are largely historical and definitely regional. One of the two talks that followed, by historian John McNeill of Georgetown, was titled “Mosquito Empires: Ecology and War in the Greater Caribbean.” Apart from the fascinating subject matter, what struck me about the symposium was the intermixing of disciplinary perspectives and the absence of Geography in the discussion. Actually the subject matter was decidedly geographic; what I mean is a room full of scholars seemed to struggle with adequate conceptual frames for discussing human history interwoven with humans’ environment.

When McNeill confessed to being “nearly a mosquito determinist,” a lively discussion followed. At issue was the tendency to refer to non-human things as actors, having agency. The case in point was mosquitos, but a more general question concerned how do we talk about human interaction with non-human and possibly determinative environmental “actors.” When the question was asked, “isn’t landscape a valuable conceptual frame here?” one response that got many heads nodding dismissed the term as being laden with negative meaning, as a (presumably elite and therefore evil) painting genre. A historian in the audience suggested “constructed spaces” was a good way to think of it.

The field of Geography has a long and tortured history in the academy. It is not unique in that regard (cf. Anthropology, History, etc.), but the particulars are interesting. Geographers study ‘Earth as home to humans.’ Most are focused on one more than the other, but nearly all are interested in the interaction between the two. Two very powerful conceptual frames are distinctive of geography, place and landscape. Both integrate the social and the “natural” (as if there were a real division there!). In occasional posts to follow, I plan to explore these, in part to better understand what makes my chosen field distinctive. I think one reason Geography departments are absent at many notable universities, including the Ivies and Stanford, is that geographers haven’t done a very good job of explaining the field.

Into the fray. In my next post, I will begin a list of factoids that bear on all this. The first will be this: Immanuel Kant lectured on two subjects for over 30 years, anthropology and geography. Quoting the blurb for the 2011 edited volume, Reading Kant’s Geography,

“Kant believed that geography and anthropology together provided knowledge of the world, an empirical ground for his thought. Above all, he thought that knowledge of the world was indispensable to the development of an informed cosmopolitan citizenry that would be self-ruling. While these lectures have received very little attention compared to his work on other subjects, they are an indispensable source of material and insight for understanding his work, specifically his thinking and contributions to anthropology, race theory, space and time, history, the environment, and the emergence of a mature public.”

Posted in digital humanities, Geography-the field | Leave a comment

Announcing the GeoHumanities Special Interest Group

siglogo-horiz_03

Today the Association of Digital Humanities Organizations (ADHO) announced the creation of a GeoHumanities Special Interest Group, instigated and co-chaired by yours truly and Kathy Weimer, Curator of Maps and the Map & GIS Coordinator at Texas A&M’s Cushing Memorial Library & Archives. We were simultaneously inspired to get this started at the recent DH2013 conference in Lincoln, after noting that several well-attended sessions featured papers with geographic, spatial-temporal, and what I call “placial” perspectives and related methodologies.

The group has a preliminary web page, a mailing list and a Twitter account, @GeoHum_SIG—please consider joining the list and following us, to keep informed about the new group’s plans and activities.

Geo or Spatial?

“My simple view is that geographic is a type of spatial.”
Michael Goodchild (pers. communication)

We have had an enthusiastic response from the couple dozen folks who reviewed a draft of our proposal to ADHO. Everyone involved has their own perspective on what “GeoHumanities” means, and I am no exception. My strong preference was for GeoHumanities over Spatial Humanities for the group name. I think when humanist scholars  refer to spatial “turns,” and “questions” they are almost always talking about geographic perspectives. The methodologies involve reasoning and computation about human activity occurring on or near the Earth’s surface, about which location over time (geographical context) is integral to the analysis or interpretation.

As a geographer and geographic information scientist, I am an unabashed advocate for those fields. I think their intellectual traditions and theoretical contributions are not well known and under-appreciated. While many prominent American universities do not have geography departments, geography is prominent in the academy. Obviously it is part and parcel of Environmental Studies and Earth Sciences. Spatial analysis and GIS are commonplace methodologies in almost all social sciences. Quantitative methods, including spatial analysis may be uncommon in the humanities overall, but not in the digital humanities. History departments and professional specializations are organized by region as well as period. The same can be said of humanist studies of literature, language, and culture. All produce geographic information and knowledge of place.

My personal focus within the GeoHumanities SIG will be in a sense to play matchmaker. Humanities scholars are frequently critical of geographic information systems, even if they have reconciled its “positivist assumptions” (as if software has assumptions!). They might quite rightly complain about an inability to handle vague and otherwise uncertain data, about simplistic notions of time, about the absence of a means for representing multivocality. But the nature of geographic information systems (lower-case) is not fixed. They are a response to requirements of certain fields and professions and software developers can only build what is specified.

Active research topics in Geographic Information Science (Geoinformatics in Europe) include extensions to temporal (cf. Bol 2013 and the latest AAG Annals) and semantic (Janowicz, Scheider & Adams 2013) representation and analysis capability, uncertainty, and managing crowd-sourced information—all of which have direct bearing on the shortcomings mentioned above. A number of researchers investigating those topics have interest in humanistic inquiry. My hope is that one function of the GeoHumanities SIG will be to make matches; to facilitate humanists making their requirements plain and encouraging efforts to for collaborations to build geospatial resources, software tools and systems that are more useful for digital humanists.

Works Cited

Yuan, M. and K. Stewart. 2008. Computation and Visualization for the Understanding of Dynamics in Geographic Domains: A Research Agenda. CRC/Taylor and Francis.

 

Posted in digital humanities | Leave a comment

Topotime: Qualitative reasoning for historical time

semi-intervals_1

Fig. 1 – Christian Freksa’s (1992) semi-intervals – Allen’s interval relations as components of temporal conceptual neighborhoods, discussed below

When my colleague Elijah Meeks recently tweeted about the possibility of a temporal topology data standard (“topotime” as he called it), my reaction was: Fantastic! Maybe the time has arrived, so to speak, for a proper Period datatype in relational databases like PostgreSQL, to meet the needs of historical scholarship—a comprehensive means for qualitative reasoning about historical time. And while we’re at it, how about a generic Period ontology design pattern that could be used in any RDFS/OWL representations?  It’s not that a start towards topotime hasn’t been made, only that we can advance things considerably if we as a community get specific about general requirements. Hmm…specifics about generality.

Our standard options in relational databases at the moment are to use one or more ISO 8601 date fields or integer fields to cobble together something that meets our immediate requirements: for example,either a single DATE or YEAR, or START and END fields in a form of either yyyy‑mm‑dd, or nnnn. We can then use the operators <, >, and = to readily compute the 13 relations of Allen’s interval algebra (before, meets, overlaps, starts, during, finishes-and their inverses-plus equals). In RDF-world, we find the Allen relations are present in CIDOC-CRM.

What more could we (humanist representers of time and temporality) possibly want? That question was the topic of a short talk I gave in a recent panel at the DH2013 in Lincoln, NE. How about a single Period field for starters—a compound date?

In fact, an existing extension for PostgreSQL written by Jeff Davis provides this (https://github.com/jeff-davis/PostgreSQL-Temporal), and I’ve used it several times. Davis provides, along with operators for standard Allen relations, several more to get finer grain, e.g. to differentiate between before (overlaps-or-left-of) and strictly-before. There are also numerous functions for computing relationships in SQL statements. A Period is entered as a date array that looks like this:

[ (yyyy-mm-dd), (yyyy-mm-dd) ]

The begin and end dates (and parts thereof) are still accessible using first(period) and last(period) functions, and these can be used in concert with PostgreSQL’s built-in date-part and interval functions to calculate periods of interest on the fly. For example, in a recent project we converted birth and death dates to Period lifetimes and calculated contemporaries as individuals who were adults ( >= 17 ) at the same time: overlaps( (first(lifetime)::date + 17 years, last(lifetime)), (1832-01-01, 1874-11-23)).

semi-intervals_2

Fig. 2 – The “survived-by” conceptual neighborhood merges several semi-interval relations

If you happen to be using PostgreSQL, this helps with many use cases, but we can and should go much further. I made a baby step in the course of dissertation research, by writing a series of Postgres functions to perform some minimal computation over Christian Freksa’s temporal conceptual neighborhoods (sets of 13 semi‑intervals) using the Period datatype. These neighborhoods are sets of semi-interval relations corresponding to some common (and not so common) reasoning tasks. For example, survived-by merges less‑than, meets, overlaps(left), starts, and during. Freksa’s algebra has many more elements which I didn’t use, but should be considered going forward.

Now, what of uncertainty in its many forms—the vague, probabilistic, and contested data we routinely encounter? The many classes of uncertainty have been outlined in a fairly exhaustive taxonomy a decade ago by historical geographer Brandon Plewe (2002), and that work should be helpful in future modeling efforts. If an event began “most likely in late Spring, 1832 (Jones 2013),” when should its representation appear in a dynamic interactive visualization having a granularity of months? When it appears in a time-filtering application, how should it differ from an event that began in “April, 1832 (Smith 2012)?”

Application logic to do something about such cases would need an underlying temporal entity having a probability (0 – 1) and/or some kind of ‘confidence’ weight. If we’re talking about the span of the event, it’s a period bounded not by instants (dates) but by periods, each with an author and probability/confidence value.

In fact some very nice research to formalize such temporal objects using periods bounded by periods has been done in the context of historical/heritage applications. Members of the FinnOnto group (Kaupinnen et al 2010) have developed a formal representation and algebra for fuzzy historical intervals (Fig. 3).

kaupinnen_1

Figure 3 – The period ‘‘from around the beginning of the 1st century B.C. to the first half of the 1st century A.D.’’ represented as a fuzzy temporal interval. The fuzzy bounds for start and end are 10- and 14-year periods respectively.

holmen_1

Fig. 4 – Deduction rule for A1 < A2, where A1, A2 are two points in time modeled as intervals.

In the realm of semantic (ontological) representations, Holmen and Ore (2009) have developed a database system based on the event-centric CIDOC-CRM that includes an algebra (Fig. 4) and temporal analyzer module to reduce fuzziness and aid in the creation of event sequences as “Stored Story Objects.” Like the previous work, period starts and ends are represented as intervals.

Ceri Binding (2009) developed a CIDOC-CRM based representation of multiple attestations of historical periods and their extents for the archaeological project, STARS.

All of the work I’ve mentioned seems to me compatible in fundamental respects. I believe that  as a community of interest can we can collaboratively develop a few shared resources that would be very helpful for many research projects. For example, a Linked Data repository of historical periods along the lines of what Pleiades/Pelagios  does for places in the Classical Mediterranean. Lex Berman of the Harvard Center for Geographical Analysis has given this a lot of thought and done some prototype work, as have others. What is the right venue for making this happen?

Another concrete goal is extending the Period datatype for PostgreSQL to allow a probability or confidence term for each bounding period. Once that is worked out, someone might even port it to ArcGIS. Yeah.

NOTE: These and related topics are among those to be addressed by a proposed new GeoHumanities SIG for the Alliance of Digital Humanities Organizations (ADHO) I’m co-instigating with Kathy Weimer of Texas A & M. Further word on that within a week or so.

Cited works

Binding, C. (2009). Implementing archaeological time periods using CIDOC CRM and SKOS. CAA 2009 Proceedings (http://hypermedia.research.southwales.ac.uk/media/files/documents/2010-06-09/ESWC2010_binding_paper.pdf)

Freksa, C. (1992). Temporal reasoning based on semi-intervals, Artificial Intelligence 54, 199-227
(http://cindy.informatik.uni-bremen.de/cosy/staff/freksa/publications/TemReBaSeIn92.pdf)

Kauppinen,T. Mantegari,G., Paakkarinen, P., Kuittinen,  H., Hyvonen, E., Bandini, S. (2010). Determining relevance of imprecise temporal intervals for cultural heritage information retrieval. International Journal of Human-Computer Studies 68 (2010) 549–560 (http://kauppinen.net/tomi/temporal-relevance-ijhcs2010.pdf)

Holmen, J., and Ore, C. (2009). Deducing event chronology in a cultural heritage documentation system. In CAA 2009 Proceedings (http://www.edd.uio.no/artiklar/arkeologi/holmen_ore_caa2009.pdf)

Plewe, B. (2002). The Nature of Uncertainty in Historical Geographic Information. Transactions in GIS, 6(4): 431-456. (http://dusk.geo.orst.edu/buffgis/TGIS_uncertainty.pdf)

Plewe, B. (2003). Representing Datum-level Uncertainty in Historical GIS. Cartography and Geographic Information Science, 30(4):319-334

Posted in computing place, research, time | 2 Comments

Place v. Space, or Why I (must) do Ontology

[Note: this post was edited June 8th to reflect an error on my part and to clarify some points about CIDOC-CRM and actual data models.]

What’s in a name? The terms space and place are often used interchangeably, by and large to no ill effect. In any given context—a particular article, an intellectual subculture—the intended meaning is clear. Moving between contexts, meaning is negotiated and understanding proceeds. “Oh, by space you mean what I call place, I see—continue.” We have to also throw in the mix the word geography used as a count noun, referring to a particular spatial configuration of people, artifacts and earth features.  I’ve just read a fine paper titled, Nazi Spatial Theory: The Dark Geographies of Carl Schmitt and Walter Christaller (Barnes and Minca 2013) which discusses territorial expansion of “Aryan space,” as “a new and violent relationship between people and space.” For many scholars with a social theoretic bent, a space is a spatial configuration of people, is a geography—I get it. Ironically enough Christaller was responsible for central place theory. By the way, I had problems with central place theory before I learned Christaller was a member of the Nazi Party, but I digress.

So if the meaning of many important terms is contextual, and we’re clever enough to sort that out, why does it matter? Because our computers are not clever enough to sort this out without our guidance and maybe greater conformity in our usage of these terms. And we are not especially consistent. Case in point:

exca_web

Çatalhöyük excavation site, Konya province, Turkey

I am currently working with data from the large Neolithic archaeological project at Çatalhöyük, Turkey, with colleague Elijah Meeks, for its director, Ian Hodder. One goal is to publish it as Linked Open Data (LOD) in order to facilitate its reuse in comparative studies and its ongoing re-interpretation over the long term. There has been increasing use of the CIDOC Conceptual Reference Model (CIDOC-CRM; Crofts et al 2011) in information systems for the cultural heritage domain very generally, including museums, digital archives, and archaeological data stores. The ResearchSpace initiative has adopted it (Mellon Foundation funded, British Museum-led). An extension developed for English Heritage (CRM-EH) will be used by the Archaeological Data Service (ADS). Open Context, the publisher of archaeological research data arguably furthest along in Semantic Web terms, is gradually implementing elements of the CRM. So it would be good if a new Çatalhöyük linked data model is harmonious with CIDOC-CRM.

CIDOC-CRM E53_Place

I like CIDOC; CIDOC is good. But in it, the concept of Place is unsettled in a few respects. A Place (E53_Place, officially) is “…extents in space, in particular on the surface of the earth, in the pure sense of physics: independent from temporal phenomena and matter.” In other words, E53_Place is what I and many others would call a location. What kinds of relations can an E53_Place have?

  • Places can have zero or more names (appellations). Check.
  • Places are where temporal things (sub-classes of E4_Period) occur and where E18_Physical_Things are located. In a manner of speaking, absolutely! In fact some have stated places are their histories and I wholeheartedly concur (Massey 2005; Janowicz 2009). But when you answer a where question with a place name like Poland or the HMS Victory, you haven’t given a location. You haven’t described “an extent in space…independent from temporal phenomena and matter” as the CIDOC scope note would suggest; both places have had many locations.
  • Places can contain, overlap, or border other Places. This is problematic. Continuing my line of thought, I would prefer to say one or more of their locations may contain, overlap or border other locations. Certain spatial footprints of the place Poland have bordered Russia at times.

CIDOC-CRM does have a class for spatial location,  a sub-class of Place Appellation (!?), E47_Spatial_Coordinates. On its face, this corresponds to Sean Gillies’ simple conceptual pattern used by the Pleiades project, which, given its increasing uptake and successful implementation, warrants adoption. But that simple pattern (Fig. 1) does not reflect the complexity we typically wish to model. What is missing from CIDOC and underspecified in Fig. 1 is the relation for which Place is the domain and Spatial Coordinates the range. Likewise there is no relation joining Place and Name. Not only do we need those asserted relations, in practice they must be reified so we can give them attributes. As the figure below indicates, both the name and location of a place are temporally indexed–”in effect” for some period. Both names and locations can result from events of various kinds (Mostern and Johnson 2008), which may or mat not be modeled. And critically, they are asserted by, or attested in, some source.

pleiades-place

Fig. 2 – The basic Pleiades Place pattern. What could be simpler? But what of the under-specified relations?

The requirement for reification of relations is not limited to has-name and has-location for Place. A similar issue faces data modelers regarding participation in events, membership in groups, and roles for both. In historical knowledge representation we’re describing assertions and possible worlds, not the things themselves. CIDOC has many classes and properties that can potentially account for this, like Information Objects and Propositional Objects, but the difficulty lies in elevating relations to “first-class” objects, which (like classes) can have properties.

300px-HMS_Victory_-_bow

HMS Victory, what a place! Where is it?

One day, in the proper forum, I will propose changing the CIDOC-CRM scope note for Place to reflect the model’s own logic and that of at least two other major systems in the domain, Pleiades and the Great Britain Historical GIS (which underlies the wonderful and place-centric web site, A Vision of Britain through Time). Namely, that a Place is a “context” and not a purely spatial entity. In an information system, it is a ‘placeholder’ that can be annotated any number of ways. Places are one kind of answer to where questions. They are where things are (or were or will be) and where things have happened (or may). Other near-synonyms include locale and setting. Places often have asserted names, with many variants. Their locations, shapes and sizes frequently change and may be contested.

After all that I grant that the point may be moot, in that increasingly we find ourselves mixing and matching elements of multiple ontologies as we develop them for our own purposes. Thing is, I would like to refer to E55_Place in the new model for Çatalhöyük–for the site itself, and for areas, spaces and buildings within it, but as long as the scope note reads the way it does, I can’t.

Places have many, many other attributes we describe them with, often entirely subjectively. That variation in description is yet another set of attributes, but that’s a topic for other posts.


Barnes, T.J. and Minca, C. (2013). Nazi Spatial Theory: The Dark Geographies of Carl Schmitt and Walter Christaller. Annals of the Association of American Geographers, 103:3, pp 669-687

Crofts, N., Doerr, M., Gill, T., Stead, S., & Stiff M. (Eds.) (2011). Definition of the CIDOC conceptual reference model, Version 5.0.4.

Janowicz, K. (2009). The role of place for the spatial referencing of heritage data. Paper presented at the Cultural Heritage of Historic European Cities and Public Participatory GIS Workshop, 17-18 Sep 2009, The University of York, UK.

Massey, D. (2005). For space. London: Sage

Mostern, R. & Johnson , I. (2008). From named place to naming event: Creating gazetteers for history. International Journal of Geographical Information Science, 22(10):1091-1108

Posted in computing place, gazetteers | Leave a comment

Computing Place: Part I

All places are small worlds: the sense of a world, however, may be called forth by art…as much as by the intangible net of human relations.
— Yi-Fu Tuan, Space and Place: Humanistic Perspective (1974, p246)

Geography is the science of place, having three high-level intertwining branches: physical geography, human geography, and geographical information science (GIScience). It seems fair to say that physical geography, like environmental sciences, encompasses or overlaps with several earth and biological science fields (geomorphology, hydrology, oceanography, meteorology; ecology, biogeography), that human geography has close associations with many of the social sciences and with urban and regional planning, and that GIScience incorporates several computational and cognitive science fields as theoretical underpinnings for geographic information systems.

Sauer_300h

Carl O. Sauer, geographer

Calling geography the science of place is somewhat provocative. Disciplinary debates over the past century have agonized over whether regional and cultural geography were too descriptive of the particularities of places (idiographic); whether we should be concerned only with the nomothetic search for general laws. As a late arrival to the debates I confess to being somewhat mystified by them. Doesn’t the analysis involved in searching for laws require rigorous description? I enjoy speculating about how Carl Sauer, the Berkeley geographer who conceived “cultural landscapes” (1925) in the 1920s, would have used today’s computational tools to describe and analyze them.

It is natural to differentiate place from space, as Yi-Fu Tuan has done in a spiritual, even mystical sense by discussing place as experiential space (Tuan 1974). There has been an unfortunate tendency amongst some critical theorists to conflate space and place in discussions of “constructed spaces;” I submit they are referring to place and that we need the two words to have distinct meanings. Spatial analysis refers to mathematical operations performed upon representations of space, conceived as objects in a void or continuous surfaces. Humanistic interpretations of space are just that, and the term place works wonderfully. Our “sense of place” is a well-known if not easily articulated concept.

So is the humanistic, experiential place computable? The name of this blog suggests my answer. I don’t think commercial geographic information systems (GIS) are especially well suited to it yet, but they can play an important part and will evolve over time. Yes, places have spatial locations—perhaps vague or contested—but that is only one of their attributes. Their other attributes  are how we know and describe them, formally or informally as containers of things (cf. Winter and Freksa 2013) or sites of events (Ibid; Grossner 2010), how we group or differentiate them, and in the case of regions or neighborhoods, how we define them.

At this stage of my research and software development adventures in computing place, I am thinking about what the measurable (computable) dimensions of place are. There are several categories of dimensions I view as equally important. The first three are commonplace; the fourth is not: (1) physical geographic settings such as land cover, terrain, and climate; (2) population characteristics like distributions of wealth and ethnicity; (3) significant human artifacts, including cities, buildings, monuments, and earthworks (“public symbols” for Tuan, which can be places themselves); and (4) activity and events.

Activity from Events of Text-making

If, instead, we conceive of a meeting-up of histories, what happens to our implicit imaginations of time and space?
— Doreen Massey, For Space (2000, p4)

For many studies, places will be most effectively described and understood in terms of what happens there and what has happened there. That is, activity and events. Certainly this seems like the most effective way to join human experience to spatial entities. The third category of dimensions listed above is directly related to events in that all artifacts are products of human acts. Among the artifacts people create are texts and images, which may be explicitly descriptive of places. Less obviously, the creations of people living in a place can be descriptive of it. Arguably, an important dimension of places like Paris, Vienna and Saint Petersburg is the conceptual content of literature emitting from their cafes and salons. Both are an excellent source for understanding places as the human experiences of space. In fact our understanding of concepts held in minds is constrained by what we can derive from language and imagery.

All texts related to places are candidates for analysis. For the recent City Nature project at Stanford (citynature.stanford.edu), we developed a topic model for the comprehensive plans of 37 large U.S. cities, to study both existing circumstances and design intent. In recent exploratory work aimed at developing a taxonomy of cultural activity, I analyzed parts-of-speech in descriptive text for UNESCO-listed intangible cultural heritage practices. Interestingly, nouns and noun phrases were a much richer source of lexical markers for activity than verbs.

chweb_words

Adams and McKenzie (2013) have developed a topic model for a large corpus of travel blog posts and Wikipedia articles about places, and demonstrated place similarity in an interactive web application, Frankenplace. Cooper and Gregory (2011) “map out the qualitative ‘data’ provided by the articulation of subjective spatial experiences” in an analysis of accounts of tours of the English Lake District by the poet, Thomas Gray, and Samuel Taylor Coleridge (1769 and 1802 respectively).

The relation between artifacts and activity is nowhere more evident than in archaeological studies. In upcoming work on knowledge representation for the Çatalhöyük project in Turkey (led by Stanford’s Ian Hodder; www.catalhoyuk.com), colleague Elijah Meeks and I will use topic models of excavation diaries and grey literature to explore the ways ancient activity is inferred from material evidence.

In forthcoming posts, I’ll elaborate on these and other projects, and discuss formal models of events and activity in places for representing cultural landscapes more explicitly.

References

Adams, B. and McKenzie, G. (2013). Inferring Thematic Places from Spatially Referenced Natural Language Descriptions. In: D. Sui, S. Elwood, and M. Goodchild (Eds.), Crowdsourcing Geographic Knowledge, pp. 201-221.

Cooper, D. and Gregory, I. N. (2011). Mapping the English Lake District: a literary GIS. Transactions of the Institute of British Geographers, Vol. 36, No. 1, p. 89-108

Grossner, K. (2010). Event Objects for Spatial History. In R. Purves, R. Weibel (Eds.) Extended Abstracts Volume, GIScience 2010, Zurich. (PDF)

Massey, D. (2005). For space. London: Sage

Sauer, C. (1925). The morphology of landscape, In J. Agnew, D.N. Livingstone, and A. Rogers, (Eds.). (1996). Human geography: an essential anthology. Cambridge, MA: Blackwell Publishers.

Tuan, Y. (1974). Space and Place: Humanistic Perspective. Progress in Geography, 6, 233-246

Winter, S. and Freksa, C. (2013). Approaching the notion of place by contrast. Journal of Spatial Information Science, Number 5, pp. 31–50

 

Posted in computing place, digital humanities, research | Tagged , , , , , , , , | Leave a comment

City Nature launches

City Nature

We’ve just launched the City Nature web site (citynature.stanford.edu), presenting some early products of research in that project centered at Stanford and UCLA. The “we” includes the project’s principal investigator, Jon Christensen, and myself and colleague Elijah Meeks as researchers-slash-developers. The full team is listed on the site. Here is a sample of what you’ll see:

cn_screen01  cn_screen02

City Nature seeks to explain the enormous variation in quantity and quality of nature in US cities – seeking first to find correlations with demographic variables (we found no strong ones), then looking to planning documents such as Comprehensive Plans, and to historical texts describing individual cities’ planning priorities and processes over time.In the first case, computational methods like topic modeling were applied; in the latter case, traditional historical research methods are employed. The two are not mutually exclusive by any means.

What you see at the site are products of an initial phase of an ambitious long-term ‘umbrella’ project (my phrase), undertaken initially at Stanford University. The products are two datasets and some digital tools for exploring them. Jon Christensen has now moved on to UCLA where he continues this work among his several other initiatives. Jon and co-PI Michael Kahan were granted the research and software development attentions of myself and colleague Elijah Meeks, who are employed by Stanford University Libraries to work on digital humanities projects in intensive year-long engagements with faculty. A dozen Stanford undergraduates with many disparate majors spent the Summer of 2012 as research assistants on these and other aspects of City Nature.

Posted in computing place, digital humanities | Leave a comment

‘Alt-Ac’ Publication

By way of introduction, I am a Digital Humanities Research Developer employed by Stanford University Libraries. Although as a recent PhD (2010, U.C. Santa Barbara Geography) I would like to have time for writing papers for peer-reviewed journals, that activity isn’t really part of my job description. Also, because I don’t aspire to teaching positions, the tenure motivation is absent. But I do want to make the work I do known to those who might have an interest, so with this post I begin the practice of occasional blogging. This seems to be standard practice for many in alternative academic careers (“alt-ac”), and I can see the merit.

All that said, I am working on chapters for two books accepted for publication that were initiated before starting my current job (Jan 2011). That indicates the kind of time frame ordinary publication channels involve. For one of these, Space in Mind: Concepts and Ontologies for Spatial Learning, I am a co-editor, along with Dan Montello and Don Janelle, both of University of California, Santa Barbara. The book will be published by MIT Press in September, 2013. Don and I are also writing a chapter, titled Concepts and Principles for Spatial Literacy.

Spatial thinking capability is strongly correlated with educational and professional performance in STEM fields, but the systematic and integrative instruction of spatial concepts, principles and reasoning skills is not an explicit goal in K-12 or college curricula. Although educators do set standards for verbal literacy, numeracy and analytical reasoning generally, there has been no comparable articulation of what it means to be spatially literate. This chapter principally concerns one component of spatial literacy, spatial conceptual knowledge. We first  report on our recent work enumerating spatial concepts which are genral across multiple fields. We then present a new formulation of spatial principles as being “composed of” those elemental concepts, which in turn inform specific spatial learning objectives. Ultimately these could help in designing course modules and lesson plans. Finally, an outline for a hypothetical undergraduate course in spatial reasoning is presented.

A second chapter, which I am co-authoring with Krzysztof (Jano) Janowicz, also of UCSB, is titled The Place of Linked Data for Historical Gazetteers. A couple of excerpts:

The world needs a freely available comprehensive digital historical gazetteer. Or rather, we need enough of them to cover all places and historical periods, with unifying interfaces permitting human and programmatic access to them. As the digitization of texts and maps proceeds at a terrific pace, and algorithms for extracting place names from them improve markedly, the enormous potential for indexing our accumulated data and knowledge on the many dimensions of place becomes more and more evident. Expressions of this need have appeared with increasing frequency in various symposia and publications.

A list of requirements for digital gazetteers from humanities scholars’ perspective was outlined recently by historian Peter Bol (2011). In this chapter we show how a system using a Linked Data approach would address nearly all of these requirements, in a prospective system that could realistically be undertaken now by a consortium of interested organizations and individuals.

…we propose a couple of additions to the Bol list, concerning (i) representation and computation for imprecise data, and (ii) a prospective disambiguation service making use of any contextual information a user provides to improve results and assist in the identity challenge so common to historical work.

 

 

Posted in bio, gazetteers, spatial literacy | Leave a comment