Vernacular Geography - Host Ireland

Vernacular Geography
Chris Jones and Florian Twaroch*
Cardiff University
Some results based on work by R. Purves (Univ. Zurich), P. Cough (Univ. Sheffield) and
H. Joho (Univ. Glasgow)
*Funded by Ordnance Survey and EC TRIPOD Project
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Vernacular Place Names
• Informal Names
– Different from Administrative names
– Or Administrative names used informally
• The Mid West (USA), South of France, Midlands,
North of England, Lake District, Wye Valley..
• The West End (London), Cardiff Bay,
The Gorbles (Glasgow)..
2

Place Names for Information Retrieval
• Specifying location on internet
– e.g. timetables, routing instructions, yellow pages, maps,
+ “local” web search
• Gazetteers used to recognise places
and
convert to quantitative footprint (point, box, polygon..)
• Most vernacular place names not recorded in
conventional gazetteers
– often no precise boundary
• Need to acquire knowledge of vernacular names
3

Sources of place name knowledge
• Gazetteers (incomplete / inadequate)
• Published maps with place annotation
– Difficult to derive footprint of vague places
– For named natural feature, can use annotation point in
association with digital terrain models to derive extent
• People
– Interviews / questionnaires – traditional methods inefficient,
but web questionnaires offer great potential
• Web documents
– Many documents mention places, addresses, postcodes
Social Web and Volunteered Geographic Information
- create map data and annotate media with place names
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Wikimapia, Open Street Map, Flikr photos…..

Spatial modelling of places on the Web
• Documents that refer to
vernacular places may also
refer to more precise places
inside them.
set of points
• Use density surface
modelling methods to map
the frequency of occurrence
of co-located places
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Summary of Web Query Method
• Submit web search engine queries referring to a
target place
• Parse resulting highest ranking web pages for
occurrence of place names
• Geocode (“ground”) place names with coordinates
• Create geometric model (surface model) and
extract approximate boundary.
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Formulating appropriate Web queries
• Region only, e.g. “Rocky Mountains”
– Retrieves all documents mentioning the name
• Region + Concept, e.g. “Hotels in Cotswolds”
– Tends to retrieve directory pages listing places
associated with the target place
• Region and lexical pattern (trigger phrase), e.g.
“Midwest towns such as xxxx”;
“xxxx in the South of France” (spatial preposition)
• Region + Concept produces highest numbers of
co-associated places in top ranking documents.
7

Geo-parsing
• Use Named Entity Recognition (NER) methods
and gazetteers to identify names
• Distinguish between geographical and nongeographical
uses – many place names occur in
organisation names and in people’s names.
• Use rules / patterns to identify these cases, e.g.
indicates person’s
name.
– Machine learning methods often used
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Geo-Parsing : true & false references
Some types of false
geographic reference
• Personal names
Smedes York
• Business name
Dorchester Hotel,
York Properties..
• Street names
Oxford Street,
London Road…
• Common words that are
also places
urban, institute, land,
battle, derby, over, well,
……
Highlighted words are present in Getty
Thesaurus of Geographic Names
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Geocoding
• Need to assign coordinate to name
• But many places have the same name
• search for co-occurrence of parent or
neighbouring places that establish
uniqueness
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Experiments with Web query method
• Queries of form “hotels in ”
submitted to Google with API
• Initially used target places that are precise –
English counties - and compared results with
known exact boundary
• Then used several target vague places and
evaluated qualitatively
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Devon (county)
Distribution of associated places Density surface at three
threshold levels (1, 0.5, 0.25
points per cell)
Thresholded
boundary compared
with actual boundary
Density surface
Note: some
places wrongly
geocoded
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4 precise places + thresholded
boundaries
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Other vague places : Mid Wales
Mid Wales
approximated with
thresholds of 0.5,
0.25 and 0.12
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Vague place: Cotswolds
Cotswolds (large region
in centre) with thresholds
of 0.5, 0.25. and 0.125
points per grid cell.
15

Vague place : Highlands of Scotland
a b c
a) Density of unique places
b) Density of number of occurrences of the name of each place
c) Density of number of documents that mention each place
Main peak corresponds to Inverness - main town in Highlands
2 nd peak in b) is due to mis-geocoding of “Cameron”
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Vague place :Mittelland (Switzerland)
Human
interpretations of
the extent
+ is the “core”
Density surface of
web mining results
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Web Screen Scraping Methods
• Gumtree – Classified Ads and Community Site
– British cities
– ads of items to sell/buy/share,+ neighbourhood names
georeferenced by UK postcodes
• Google Maps Local Businesses
– United Kingdom
– business addresses, georeferenced by UK postcodes
• Google Maps User Created Contents
– United Kingdom
– users can enter point, line and polygon features to
describe local places using an interface to Google Maps
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Ads in Gumtree for Cardiff
Neighbourhood
name
+
Postcode
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Vernacular use of Cardiff community
(parish) names from Gumtree and Google
Usage of Plasnewydd coincides with boundary
Usage of Roath does not coincide with boundary and
subsumes Plasnewydd
Plasnewydd
Plasnewydd
Roath
Roath
Cathays Splott
Boundarires based on Ordnance Survey Data
© Crown Copyright 2008
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Future work
• Web query method for regions
– different sorts of queries to cover places where few
settlements
• Screen scraping methods for neighbourhoods
– Evaluation of quality of different sources
• Improve methods for thresholding surfaces
• Use multiple types of data, e.g. population, landcover..
• Discover vernacular names on Web
• Web questionnaires to elicit people’s understanding of
the interpretation of vernacular names
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