Implementing an Open Source Spatiotemporal Search Platform for Spatial Data Infrastructures

Implementing an Open Source
Spatiotemporal Search Platform for
Spatial Data Infrastructures
OGRS 2016, Perugia, Italy - 10/13/2016
Paolo Corti [1]
, Benjamin Lewis [1]
, Athanasios Tom Kralidis [2]
,
Ntabathia Jude Mwenda [1]
[1] Harvard Center for Geographic Analysis
[2] Open Source Geospatial Foundation

HHypermap (Harvard Hypermap)
● With Funding from the National Endowment for the
Humanities, the Harvard Centre for Geographic
Analysis (CGA) developed HHypermap, a map
services registry and search platform
● HHypermap was developed in the process of
re-engineering the search component of CGA’s public
domain SDI (WorldMap http://worldmap.harvard.edu),
based on GeoNode
● It is built on an open source software stack

A brief history
WorldMap was developed on GeoNode 1.2 and released in 2012 as a public space for
scholars and the public to upload and share spatial data.
Within a year WorldMap had 12,000 datasets and 8000 users.
Finding data became difficult and demand grew for being able to bring in and save
map service layers from other servers.
The CGA proposed to NEH to build a system for building and maintaining a
comprehensive registry of WMS and Esri REST Map services that would plug into
Worldmap.
Being able to search for data by time and space as well as by keyword was a priority
given the user base.

Note on uptake
Since the release of HHypermap on GitHub in April, a U.S. federal agency has adopted
it and Boundless is using it within its flagship platform Boundless Exchange.
The geo-visualization capabilities we developed (2D faceting in Lucene) are highly
scalable and being used to build a “Billion Object Platform” to enable interactive
exploration of a billion spatio-temporal objects.

Glossary
● Spatial Data Infrastructure (SDI)
● Catalogue Service for the Web (CSW)
● Search Engine

Spatial Data Infrastructure (SDI)
A Spatial Data Infrastructure
(SDI) is a framework of
geospatial data, metadata, users
and tools intended to provide
an efficient and flexible way to
use spatial information

Catalogue Service for the Web (CSW)
● One of the key software components of an SDI is the catalogue service
which is needed to discover, query, and manage the metadata
● Catalogue services in an SDI are typically based on the Open Geospatial
Consortium (OGC) Catalogue Service for the Web (CSW) standard
which defines common interfaces for accessing the metadata information
● Notable implementations: pycsw, GeoNetwork

Search Engine
● A search engine is a software system capable of supporting fast and
reliable search
● It provides features such as full text search, natural language processing,
weighted results, fuzzy tolerance results, faceting, hit highlighting
● Highly scalable and replicable architecture
● Notable implementations: Solr and ElasticSearch, both based on Apache
Lucene

Goals of HHypermap
● Provide a framework for building and maintaining a comprehensive registry of
web map services
● Support modern search capabilities such as spatial and temporal faceting and
instant previews via an open API
● Behind the scenes HHypermap scalably harvests OGC and Esri service metadata
from distributed servers, organizes that information, and pushes it to a search
engine
● Monitor services and layers for reliability and use to improve results ranking
● End users can search the SDI metadata using standard interfaces provided by the
internal CSW catalogue, and benefit from advanced search capabilities provided
by a more full featured, RESTful API

Catalogue Service for the Web
● The OGC Catalogue Service for the Web (CSW) standard specify the interfaces
and bindings, as well as a framework for defining the application profiles required
to publish and access digital catalogues of metadata for geospatial data and
services
● Based on the Dublin Core metadata information model, CSW supports broad
interoperability around discovering geospatial data and services spatially,
non-spatially, temporally, and via keywords or freetext
● CSW supports application profiles which allow for information communities to
constrain and/or extend the CSW specification to satisfy specific discovery
requirements and to realize tighter coupling and integration of geospatial data
and services

Limitations of CSW
CSW provides numerous benefits to SDI’s, but there are numerous opportunities to
enhance the functionality of CSW and the server implementations of CSW by adding
in standard search engine features. Some examples:
● Faceted search
● JSON representation (vs XML in CSW)
● Simplified query interface (CSW, being based on XML, can quickly become
complex)
● Text stemming (ability to detect words derived from a common root)
● Highly scalable and replicable architecture

The Need for Search Engines in Spatial Data
Infrastructure
● Numerous types of web application such as CMS, Wikis, data delivery frameworks,
all benefit from improved data discovery
● In the last few years, these applications have delegated the task of search
optimization to specific frameworks known as search engines
● Rather than implementing a custom search logic, these platforms now often add a
search engine in the stack to improve search
● Apache Solr and Elasticsearch, two popular open source search engine web
platforms, and both based on Apache Lucene, can now be part of a typical CMS
stack to support complex search criteria, faceting, result highlighting, query
spellcheck, relevance tuning and more
● As for CMS, SDI search can dramatically benefit if paired with these platforms

How a search engine works
Two distinct phases:
● Indexing: all of the documents (metadata, in the SDI context) that must be
searched are scanned, and a list of search terms (an index) is built. For each search
term, the index keeps track only of the identifiers of the documents that contain
the search term
● Searching: only the index is looked at, and a list of the documents containing the
given search term is quickly returned to the client. This indexed approach makes a
search engine extremely fast in outputting results

Benefits from search engine frameworks
● Very fast, thanks to the indexing mechanism
● Handling the ambiguities of natural languages, with stop words (words filtered out
during the processing of text), stemming (ability to detect words derived from a
common root), synonyms detection, and controlled vocabularies such as thesauri and
taxonomies
● Phrase searches and proximity searches (search for a phrase containing two different
words separated by a specified number of words)
● Weighted results
● Handling regular expressions, wildcard search, and fuzzy search to provide results for a
given term and its common variations
● Support for boolean queries (AND, OR, NOT)
● Hit highlighting
● Highly scalable and replicable

Faceted search
● Faceting is the arrangement of search results in
categories based on indexed terms
● This capability makes it possible for example, to
provide an immediate indication of the number of
times that common keywords are contained in
different metadata documents
● A typical use case for SDI is with metadata
categories, keywords and regions
● Faceting without a search engine is generally
computationally expensive in relational normalized
structures (lots of query in a RDBMS)

Temporal faceting
● Search engines can also support
temporal and spatial faceting, two
features that are extremely useful for
browsing large collections of
geospatial metadata.
● Temporal faceting can display the
number of metadata documents in a
SDI by date range as a kind of
histogram

Spatial faceting
● Spatial faceting can provide a
spatial surface representing
the distribution of layers or
features across an area of
interest
● In this example a heatmap is
generated by spatial faceting
to show the distribution of
layers in the WorldMap SDI
for a given geographic region

HHypermap: an SDI search engine based on Free and
Open Source Software
● HHypermap is an application that manages OGC web services (such as WMS,
WMTS), and Esri REST endpoints
● map service crawling, and harvesting, and uptime statistics gathering for remote
services and layers
● The aim of HHypermap is to provide a more effective search experience to
WorldMap users and also for users outside WorldMap
● WorldMap is an open source mapping platform, based on GeoNode, developed
by the CGA to lower the barrier for scholars who wish to explore, visualize, edit
and publish geospatial information

HHypermap Architecture
Built on Open Source
software:
Celery, RabbitMQ,
Django, Lucene (Solr,
Elasticsearch), MapProxy,
Memcached, OWSLib,
PostgreSQL, PostGIS,
pycsw

Future Work
● While the CSW 3.0.0 standard provides improvements to address mass
market search/discovery, the benefits of search engine implementations
combined with broad interoperability of the CSW standard present
opportunities to integrate the CSW standard with search engine
methodologies
● The authors hope that such an approach will become formalized as a
CSW Application Profile or Best Practice in order to achieve maximum
benefit and adoption in SDI activities
● This will allow CSW implementations to make better use of search engine
methodologies for improving the user search experience in SDI
workflows

Conclusion
HHypermap aims to provide a FOSS solution using modern
approaches to realize a highly scalable, flexible and robust geospatial
registry and catalogue/search platform while achieving broad
interoperability via open standards

References
● Harvard University CGA: http://gis.harvard.edu/
● WorldMap: http://worldmap.harvard.edu/
● Harvard Hypermap public registry: http://hh.worldmap.harvard.edu/
● HHypermap code repository: https://github.com/cga-harvard/HHypermap

Implementing an Open Source Spatiotemporal Search Platform for Spatial Data Infrastructures

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