Geography Reference
In-Depth Information
number of searching and matching operations which grow exponentially
when the number of countries increases. Currently the algorithm works one
country at a time so it is not that time consuming but when run on global
datasets, it is going to take days to complete which is not desirable. Hence
faster processing techniques need to be explored and researched. Distributed
processing and acquiring facilities for faster processing research of joining
multiple datasets so outside channeling of work is the viable option. The
Distributed Processing Hadoop Lab is a feasible research facility for under-
taking the resolution of above limitations. The utility of the approach lies in
identifying places having names in multiple languages, scripts and popular
names, official names, and nicknames. So complete matching, partial
matching, fuzzy matching have been employed. For example related to a
city such as Prague in OSM the properties of the city is bound to be scattered
across naming conventions in local language and urban expansions of Prague.
In such a scenario getting 100 % match is a commendable achievement and it
has been achieved in this paper. No simple or straight forward algorithm
could have done that. In a single country like India, where the dialect changes
every 200 km, GeoNames contains information of a place in India in a lot of
ways and this is true for OSM, GoogleMap and a lot of others. It is true that a
lot of places at present are not ranked but that will change very soon with
addition of data. We need to improve the ranking constantly. To some extent
the paper has achieved cartographic results for the better where more compre-
hensive information was extracted from geodatasets towards ranking cities.
By no means is the task over since to proceed towards the important decisions
of “Which of these labels should be visible?” and “how much should this
label be emphasized?” only partial breakthroughs have been achieved in this
paper. To do further future task is to join additional information. One more
advancement envisioned is to place the joined datasets in GeoServer to
integrate with the Graphical Interface of World Maps.
Acknowledgments The Ministry of Education, Youth and Sports of the Czech Republic, Project
CZ.1.07/2.3.00/30.0021 “Strengthening of Research and Development Teams at the University of
Pardubice”, financially supported this work.
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