Under the Hood: How Geonames Aggregates Over 35 Sources into One Data Set

•

4 likes•2,059 views

GeoNames aggregates geographic data from many sources into a single dataset. It contains over 6.5 million geographic features. Marc Wick explains how GeoNames collects data from national mapping agencies, statistical offices, and user contributions. GeoNames makes this data freely available through daily data dumps, web services, and libraries for developers to use in their applications.

Technology Travel

GeoNames
“Under the Hood: How GeoNames Aggregates
many Sources into One Data Set“

GeoNames is ...
aggregator of free geo data

I am ...
Marc Wick
self employed software engineer, Switzerland

GeoNames Feature Density Map

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 2

GeoNames - Gazetteer
Pragmatic, useful, ease of use
Over 6.5 million features
Cc-by licence
9 feature classes

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 3

Screen shot Berlin

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 4

Origins and Goal
Proprietary application
Team up together
contribute modifications to central data base.
applications switch to GeoNames from
proprietary aggregation

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 5

Challenge
A lot of data IS
available
Many providers
Languages
Scripts

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 6

GeoNames Ambassadors
GeoNames contact
Speak local language
Know local situation

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 7

Data Sources
National Mapping Agencies
Statistical Offices
Postal codes
National Geospatial-Intelligence Agency (NGA)‫‏‬
Applications using GeoNames
− Data files
− Manual modifications

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 8

US vs Europe
US data is freely available
European data is not available
Rest of the World?
Consequences

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 9

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 10

Future of geodata availability
We believe basic geodata will be free in most
countries

Why :
− Economy
− Traffic Policy and Road Safety (road signs)‫‏‬

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 11

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 12

Free Availability is only a First Step

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 13

Who aggregates data
GeoNames
Super national mapping agencies
Super national organisations

INSPIRE

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 14

Problems and Solutions I
Shape / GML FWTools/ GDAL/OGR
Datum reprojection Postgis/epsg/native
tools/custom impl

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 15

Problems and Solutions II
FeatureCodes not 1:1 Pattern matching
non-ASCII Transliteration
Country codes
Admin1 codes

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 16

Place name matching
Geocoding
Distance
feature type and feature code
Reverse geocoding, compare name similarity
− levenshtein distance
− letter pair similarity

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 17

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 18

$Wikipedia GeoTemplates Proliferation of GeoFormats No consensus, Anarchy Examples − <geo>48 46 36 N 121 48 51 W</geo> − {{coor d|48.7767|N|121.8142|W|}} − Berlin : |lat_deg = 52|lat_min = 31 − ... (Any template you could possibly think of is used somewhere)‫‏‬ GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 19$

Alternate Names

...
Italian : Berlino
English : Berlin
Arabic : ‫نيلرب‬
Korean :
Thai : เบอรลิน
Russian : Берлин
Chinese :
Marathi : बर् लि न
... (ca 100 names)‫‏‬
GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 20

Postal codes
Geocode – postal code numeric distance
Accuracy, completeness

ScribbleMaps by Robert Kosara

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 21

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 22

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 23

Data Dump
Flat csv files
Simple format
Ease of use
Full daily dump
daily modifications
rdf

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 24

Web Services
Search
− Ranking
Tf idf
Relevancy
− I18n

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 25

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 26

Hierarchy Web Services
Hierarchy
Child
Neighbour
Sibling

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 27

Apache

mod rewrite

ROME (RSS)‫‏‬ jdom.org (xml)‫ ‏‬JSON

Tomcat (Java)‫‏‬ JMS
activeMQ

Lucene

SRTM3
Gtopo30
JDBC
Full Text Index
TF-IDF

Database : Postgres
(postgis)‫‏‬

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 28

Libraries
Java
Drupal
Ruby
Php
Perl
Python
Lisp

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 29

Synchronization
Dail dump
Daily modification
Jms

Rdf dump, periodically

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 30

Linked Data

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 31

Applications using GeoNames
thousands of applications
search
Site navigation
geo-coding

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 32

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 33

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 34

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 35

Thank you for your attention.

GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 36

The presentation will describe how to setup a production system based on GeoServer from the points of view of performance, availability and security. The suggestions will start covering how a single node GeoServer should be prepared for internet usage, tuning logging, connection pools, security, data and JVM preparation, keeping disk, memory and CPU usage in check within the limits of the available resources. We’ll then move to tools used to monitor the production instances, ranging from probes to request auditing and watch-dogs. Finally the presentation will cover setting up a cluster of server and the strategies for keeping them in synch, from the traditional multi-tier setup (testing vs production) to the systems that need to keep an ever evolving catalog of layers constantly on-line and in synch.

State of GeoServer - FOSS4G 2016

GeoSolutions

State of GeoServer provides an update on our community and reviews the new and noteworthy features for the Project. The community keeps an aggressive six month release cycle with GeoServer 2.8 and 2.9 being released this year. Each releases bring together exciting new features. This year a lot of work has been done on the user interface, clustering, security and compatibility with the latest Java platform. We will also take a look at community research into vector tiles, multi-resolution raster support and more. Attend this talk for a cheerful update on what is happening with this popular OSGeo project. Whether you are an expert user, a developer, or simply curious what these projects can do for you, this talk is for you.

Raster data in GeoServer and GeoTools: Achievements, issues and future develo...

GeoSolutions

GeoNetwork, The Open Source Solution for the interoperable management of ge...GeoSolutions

GeoServer on steroids

GeoSolutions

GeoServer on Steroids

GeoSolutions

MapStore Create, save and share maps and mashups @ GRASS-GFOSS 2013

GeoSolutions

W3C における IPv6 への取り組み

Rikkyo University

With advances in computer hardware such as 10 gigabit network cards, infiniband, and solid state drives all becoming commodity offerings, the new bottleneck in big data technologies is very commonly the processing power of the CPU. In order to meet the computational demand desired by users, enterprises have had to resort to extreme scale out approaches just to get the processing power they need. One of the most well known technologies in this space, Apache Spark, has numerous enterprises publicly talking about the challenges in running multiple 1000+ node clusters to give their users the processing power they need. This talk is based on work completed by NVIDIA’s Applied Solutions Engineering team. Attendees will learn how they were able to GPU-accelerate UDFs in PySpark using open source technologies such as Numba and PyGDF, the lessons they learned in the process, and how they were able to accelerate workloads in a fraction of the hardware footprint.

NCGIC The Geospatial Revolution

Peter Batty

Scalable Data Analytics and Visualization with Cloud Optimized Services

Globus

Introduction to NoSQL with Couchbase

Tugdual Grall

Big Data Seervices in Danaos Use Case

Big Data Value Association

Geoprocessing with Neo4j-Spatial and OSM

Craig Taverner

FOSS4G 2011 Presentation What better way to perform geoprocessing than on a graph! And what better dataset to play with than Open Street Map! Since we presented Neo4j Spatial at FOSS4G last year, our support for geoprocessing functions and for modeling, editing and visualization of OSM data has improved considerably. We will discuss the advantages of using a graph database for geographic data and geoprocessing, and we will demonstrate this using the amazing Open Street Map data model.

Giving MongoDB a Way to Play with the GIS Community

MongoDB

The Geographic Information System (GIS), industry is booming, especially with the continued reliance on online maps and the rise of location-aware mobile devices. GIS tech can be one of the key players in the mobile internet, big data, and the internet of things, and is an essential tool for the next generation of the global IT industry. Yet, the GIS community is not prepared. With all the data available, GIS experts lack an off-the-shelf solutions to manage the growing volume of spatial data. Relational spatial databases (RSDB) were the leader in this field for decades, but RSDBs have failed to innovate to handle massive volumes of data coming in at high velocity. Fortunately, MongoDB a useful tool for this challenge, but needs some tooling to create a connector to the GIS tech ecosystem. In order to bridge the gap, we built a pipeline to comply with the architecture of the Geospatial Data Abstraction Library (GDAL), so that MongoDB can work with most of popular GIS tools such as OpenLayers, Mapserver, GeoServer, QGIS, ArcGIS and others with ease. In this talk, I'll go through this pipeline tool and showcase some examples of how you can use this in your next application.

Seedcamp Overview

adunne

Netvibes Preview

adunne

Similar to Under the Hood: How Geonames Aggregates Over 35 Sources into One Data Set

The Construction of the Internet Geological Data System Using WWW+Java+DB Tec...

Channy Yun

Comparing Vocabularies for Representing Geographical Features and Their Geometry

Ghislain Atemezing

ITCVMark Sharman

GeoKnow: Making the Web an Exploratory Place for Spatial Data

OpenLink Software

Scalable Web Architectures: Common Patterns and Approaches

adunne

GIS in the Rockies Geospatial Revolution

Peter Batty

Devteach 2017 Store 2 million of audit a day into elasticsearch

Taswar Bhatti

OSGeo Live Lightening Overview

Jody Garnett

PCIC Data Portal 2.0

James Hiebert

Instalação geo ipCarlos Eduardo

Open Source Databases And Gis

Kudos S.A.S

The User-participated Geospatial Web as Open Platform

Channy Yun

Softshake 2013: Introduction to NoSQL with Couchbase

Tugdual Grall

GPU-Accelerating UDFs in PySpark with Numba and PyGDF

Keith Kraus

NCGIC The Geospatial Revolution

Peter Batty

Scalable Data Analytics and Visualization with Cloud Optimized Services

Globus

Introduction to NoSQL with Couchbase

Tugdual Grall

Big Data Seervices in Danaos Use Case

Big Data Value Association

Geoprocessing with Neo4j-Spatial and OSM

Craig Taverner

Giving MongoDB a Way to Play with the GIS Community

MongoDB

Similar to Under the Hood: How Geonames Aggregates Over 35 Sources into One Data Set (20)

The Construction of the Internet Geological Data System Using WWW+Java+DB Tec...

Comparing Vocabularies for Representing Geographical Features and Their Geometry

ITCV

GeoKnow: Making the Web an Exploratory Place for Spatial Data

Scalable Web Architectures: Common Patterns and Approaches

GIS in the Rockies Geospatial Revolution

Devteach 2017 Store 2 million of audit a day into elasticsearch

OSGeo Live Lightening Overview

PCIC Data Portal 2.0

Instalação geo ip

Open Source Databases And Gis

The User-participated Geospatial Web as Open Platform

Softshake 2013: Introduction to NoSQL with Couchbase

GPU-Accelerating UDFs in PySpark with Numba and PyGDF

NCGIC The Geospatial Revolution

Scalable Data Analytics and Visualization with Cloud Optimized Services

Introduction to NoSQL with Couchbase

Big Data Seervices in Danaos Use Case

Geoprocessing with Neo4j-Spatial and OSM

Giving MongoDB a Way to Play with the GIS Community

Recently uploaded

State of ICS and IoT Cyber Threat Landscape Report 2024 preview

Prayukth K V

The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development. The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers: State of global ICS asset and network exposure Sectoral targets and attacks as well as the cost of ransom Global APT activity, AI usage, actor and tactic profiles, and implications Rise in volumes of AI-powered cyberattacks Major cyber events in 2024 Malware and malicious payload trends Cyberattack types and targets Vulnerability exploit attempts on CVEs Attacks on counties – USA Expansion of bot farms – how, where, and why In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East Why are attacks on smart factories rising? Cyber risk predictions Axis of attacks – Europe Systemic attacks in the Middle East Download the full report from here: https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/

From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...

Product School

Encryption in Microsoft 365 - ExpertsLive Netherlands 2024

Albert Hoitingh

Bits & Pixels using AI for Good.........

Alison B. Lowndes

When stars align: studies in data quality, knowledge graphs, and machine lear...

Elena Simperl

FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf

FIDO Alliance

Neuro-symbolic is not enough, we need neuro-*semantic*

Frank van Harmelen

Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”. All of this illustrated with link prediction over knowledge graphs, but the argument is general.

Monitoring Java Application Security with JDK Tools and JFR Events

Ana-Maria Mihalceanu

Key Trends Shaping the Future of Infrastructure.pdf

Cheryl Hung

GraphRAG is All You need? LLM & Knowledge Graph

Guy Korland

Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs. 1. Unifying Large Language Models and Knowledge Graphs: A Roadmap. https://arxiv.org/abs/2306.08302 2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs: https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/

Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024

Tobias Schneck

As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other? Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Paul Groth

Transcript: Selling digital books in 2024: Insights from industry leaders - T...

BookNet Canada

The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more. Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/ Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.

The Art of the Pitch: WordPress Relationships and Sales

Laura Byrne

Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes? All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.

Elevating Tactical DDD Patterns Through Object Calisthenics

Dorra BARTAGUIZ

After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!

Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality

Inflectra

In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring. Learn about: • The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks. • Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective. • Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification. • Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process. Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.

FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf

FIDO Alliance

DevOps and Testing slides at DASA Connect

Kari Kakkonen

Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...

UiPathCommunity

💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™: See how to accelerate model training and optimize model performance with active learning Learn about the latest enhancements to out-of-the-box document processing – with little to no training required Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath. Speakers: 👨‍🏫 Andras Palfi, Senior Product Manager, UiPath 👩‍🏫 Lenka Dulovicova, Product Program Manager, UiPath

FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf

FIDO Alliance

Recently uploaded (20)

State of ICS and IoT Cyber Threat Landscape Report 2024 preview

From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...

Encryption in Microsoft 365 - ExpertsLive Netherlands 2024

Bits & Pixels using AI for Good.........

When stars align: studies in data quality, knowledge graphs, and machine lear...

FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf

Neuro-symbolic is not enough, we need neuro-*semantic*

Monitoring Java Application Security with JDK Tools and JFR Events

Key Trends Shaping the Future of Infrastructure.pdf

GraphRAG is All You need? LLM & Knowledge Graph

Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Transcript: Selling digital books in 2024: Insights from industry leaders - T...

The Art of the Pitch: WordPress Relationships and Sales

Elevating Tactical DDD Patterns Through Object Calisthenics

Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality

FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf

DevOps and Testing slides at DASA Connect

Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...

FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf

Under the Hood: How Geonames Aggregates Over 35 Sources into One Data Set

1. GeoNames “Under the Hood: How GeoNames Aggregates many Sources into One Data Set“ GeoNames is ... aggregator of free geo data I am ... Marc Wick self employed software engineer, Switzerland

2. GeoNames Feature Density Map GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 2

3. GeoNames - Gazetteer Pragmatic, useful, ease of use Over 6.5 million features Cc-by licence 9 feature classes GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 3

4. Screen shot Berlin GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 4

5. Origins and Goal Proprietary application Team up together contribute modifications to central data base. applications switch to GeoNames from proprietary aggregation GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 5

6. Challenge A lot of data IS available Many providers Languages Scripts GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 6

7. GeoNames Ambassadors GeoNames contact Speak local language Know local situation GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 7

8. Data Sources National Mapping Agencies Statistical Offices Postal codes National Geospatial-Intelligence Agency (NGA)‫‏‬ Applications using GeoNames − Data files − Manual modifications GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 8

9. US vs Europe US data is freely available European data is not available Rest of the World? Consequences GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 9

10. GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 10

11. Future of geodata availability We believe basic geodata will be free in most countries Why : − Economy − Traffic Policy and Road Safety (road signs)‫‏‬ GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 11

12. GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 12

13. Free Availability is only a First Step GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 13

14. Who aggregates data GeoNames Super national mapping agencies Super national organisations INSPIRE GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 14

15. Problems and Solutions I Shape / GML FWTools/ GDAL/OGR Datum reprojection Postgis/epsg/native tools/custom impl GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 15

16. Problems and Solutions II FeatureCodes not 1:1 Pattern matching non-ASCII Transliteration Country codes Admin1 codes GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 16

17. Place name matching Geocoding Distance feature type and feature code Reverse geocoding, compare name similarity − levenshtein distance − letter pair similarity GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 17

18. GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 18

19. Wikipedia GeoTemplates Proliferation of GeoFormats No consensus, Anarchy Examples − <geo>48 46 36 N 121 48 51 W</geo> − {{coor d|48.7767|N|121.8142|W|}} − Berlin : |lat_deg = 52|lat_min = 31 − ... (Any template you could possibly think of is used somewhere)‫‏‬ GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 19

20. Alternate Names ... Italian : Berlino English : Berlin Arabic : ‫نيلرب‬ Korean : Thai : เบอรลิน Russian : Берлин Chinese : Marathi : बर् लि न ... (ca 100 names)‫‏‬ GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 20

21. Postal codes Geocode – postal code numeric distance Accuracy, completeness ScribbleMaps by Robert Kosara GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 21

22. GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 22

23. GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 23

24. Data Dump Flat csv files Simple format Ease of use Full daily dump daily modifications rdf GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 24

25. Web Services Search − Ranking Tf idf Relevancy − I18n GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 25

26. GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 26

27. Hierarchy Web Services Hierarchy Child Neighbour Sibling GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 27

28. Apache mod rewrite ROME (RSS)‫‏‬ jdom.org (xml)‫ ‏‬JSON Tomcat (Java)‫‏‬ JMS activeMQ Lucene SRTM3 Gtopo30 JDBC Full Text Index TF-IDF Database : Postgres (postgis)‫‏‬ GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 28

29. Libraries Java Drupal Ruby Php Perl Python Lisp GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 29

30. Synchronization Dail dump Daily modification Jms Rdf dump, periodically GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 30

31. Linked Data GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 31

32. Applications using GeoNames thousands of applications search Site navigation geo-coding GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 32

33. GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 33

34. GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 34

35. GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 35

36. Thank you for your attention. GeoNames, Marc Wick Web 2.0 Expo - 8. Nov 2007 Berlin 36

Under the Hood: How Geonames Aggregates Over 35 Sources into One Data Set

Recommended

Recommended

More Related Content

Similar to Under the Hood: How Geonames Aggregates Over 35 Sources into One Data Set

Similar to Under the Hood: How Geonames Aggregates Over 35 Sources into One Data Set (20)

More from adunne

More from adunne (20)

Recently uploaded

Recently uploaded (20)

Under the Hood: How Geonames Aggregates Over 35 Sources into One Data Set