In this paper we propose a solution to several key limitations of current web based mapping systems: slow rendering speeds and the restriction of online map viewing to a small number of areal units as well as a limited number of users. Our approach is implemented as a Scalable Tile Map Service that distributes dynamic choropleth maps in real-time through a new caching methodology. This new Map Service lays the foundation for advances in web based applications reliant on dynamic map rendering such as emergency management systems and interactive exploratory spatial data analysis. We present the results of an empirical illustration in which this new methodology is used to facilitate collaborative decision making by visualizing spatial outcomes of simulation results on the fly.

Boris Dev

Charles R. Schmidt

English

Research Papers in Economics

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Working Paper 2008-13 
 
 
A Scalable Tile Map Service for Distributing Dynamic Choropleth Maps 
 
Charles R. Schmidt and Boris Dev A Scalable Tile Map Service for Distributing Dynamic Choropleth Maps
Charles R. Schmidt
GeoDa Center
School of Geographical Sciences
Arizona State University
Charles.R.Schmidt@asu.edu
Boris Dev
Department of Geography
San Diego State University
bdev@rohan.sdsu.edu
Abstract
In this paper we propose a solution to several key lim-
itations of current web based mapping systems: slow ren-
dering speeds and the restriction of online map viewing to
a small number of areal units as well as a limited num-
ber of users. Our approach is implemented as a Scal-
able Tile Map Service that distributes dynamic choropleth
maps in real-time through a new caching methodology.
This new Map Service lays the foundation for advances in
web based applications reliant on dynamic map rendering
such as emergency management systems and interactive ex-
ploratory spatial data analysis. We present the results of
an empirical illustration in which this new methodology is
used to facilitate collaborative decision making by visualiz-
ing spatial outcomes of simulation results on the ﬂy.
1. Introduction
Oneofthemostfrequentlyusedfeaturesofacommercial
Geographic Information System (GIS) is its ability to efﬁ-
ciently generate a choropleth map that the user can interact
with to explore her data by changing the variates mapped,
zooming to different geographic scales, and panning across
locations. Web based mapping approaches translate this
functionality to the distributed networks of the Internet.
However, several key limitations remain, notably a lack of
speed in map display and interaction, as well as being able
to only display maps with a small number of areal units (e.g.
under 1,000), and for small numbers of users.
Overcoming these limitations is essential as the founda-
tion for a variety of sophisticated time- and scale-sensitive
applications. For example, speed is a crucial factor in the
This material is based upon work supported by the National Science
Foundation under Grant No. SES-0345945, Decision Center for a Desert
City (DCDC). Any opinions, ﬁndings and conclusions or recommendation
expressed in this material are those of the author(s) and do not necessarily
reﬂect the views of the National Science Foundation (NSF).
delivery of Internet maps based on real-time shared geo-
graphic disaster assessment data used in relief efforts. Re-
searchers have identiﬁed real-time mapping applications
and more user-friendly interfaces as limitations of existing
geospatial technologies to applications in disaster and emer-
gency management [1, 3].
Addressing these limitations will help to advance wide-
spread adoption of geospatial technologies as effective tools
for disaster and emergency management. For instance,
[2] highlights the importance of Community Vulnerability
Maps to all levels of disaster and hazard mitigation and
management. Enablingthelinkageofreal-timedatatothese
maps could prove to be an important tool in this context.
Further, integration of simulation results with maps could
enable real-time exploration of different planning or disas-
ter scenarios, allowing stake holders to identify areas prone
to risk. We present an empirical illustration of this notion
involving the simulation of water demand and supply later
in this paper.
Further, the rapid success of products such as Google
Earth and Google Maps (including its choropleth mapping
capabilities) may be an indicator of future demand for more
sophisticated exploratory spatial analysis visualization tools
over the Internet. However, the limitations with existing
choropleth mapping rendering currently present an obstacle
to such advances. In particular, our efforts are motivated
by the emerging ﬁeld of interactive exploratory spatial data
analysis (ESDA), including our work with the open source
software STARS (Space Time Analysis of Regional Sys-
tems) [5].
A major feature of interactive ESDA software is the link-
ing of statistical plots to choropleth maps. For instance,
in STARS a user can travel through a time-series plot of
a spatially aggregate variable (e.g., income in U.S. states)
while simultaneously observing a choropleth map of the
disaggregated variable (e.g., income in U.S. counties) as it
evolves over time. Improvements in choropleth map render-
ing speeds is essential for such interactive ESDA analysis:
In order for users to be able to experiment with alternativescales, variates, and plots in an interactive context, maps
need to be rendered on the ﬂy. This is currently not possible
with existing applications.
One of the objectives of this research is to introduce
the speed and ease of basic GIS visualization capabilities
for coloring objects on a map based on membership in a
predeﬁned classiﬁcation from the PC to the web browser.
What distinguishes our Scalable Tile Map Service from ex-
isting web based mapping applications is that interesting
data structure designs and visualizations can be shared and
extended dynamically in a timely manner over the Internet
with all interested parties. This is not possible with a static
JPEG map saved from a Google Earth type product. Sec-
ond, weproposeanopensourceapproachtomappingdevel-
opment technology. Finally, we require no software other
than a web browser to be installed on the internet client de-
vice.
Our approach to improving choropleth map rendering
over the Internet lays the groundwork for advances in fu-
ture web based geovisualization tools and geographic data
repository projects. It is a tool that reﬂects the spirit of open
source and object oriented code development. After out-
lining the research problems associated with current web
based mapping, this paper details our proposed solution to
these problems. We illustrate the implementation of our
Scalable Tiled Map Service with an empirical example be-
fore concluding with future extensions.
2. Research Problem
The performance of any new method for achieving this
goal can be broadly understood using three parameters: 1)
the speed at which the choropleth maps are drawn to a
browser after a new variable or new location has been se-
lected for viewing by a user 2) the size of the number of
areal units that is capable of being drawn to the browser
in a reasonable time and 3) the simplicity of the code to
be understood by new open source developers so that on-
going performance is improved and new geovisualization
tools can be more quickly developed and tested to extend
the value of such Internet web data mapping projects.
Traditional web based mapping uses a client-server
based process in which the client makes a request to the
server, the server renders the request and returns an image
which is displayed to the user. This process is highly in-
efﬁcient and a typical small server can only handle a few
simultaneous clients before it becomes noticeably less re-
sponsive. On the client side this process introduces very
limiting usability issues. Each time the user pans the map a
new request is made and the client must wait for the server
to respond with the new image. To overcome these limi-
tations current web based mapping systems pre-render the
maps into tiles within a set of ﬁxed scales. With all the
necessary information cached the servers are able to handle
larger volumes of users.
With knowledge of this tiling scheme the client can re-
quest only the tiles it needs to display in response to the
user’s request. This also enables the user to pan the map
more dynamically as only the edges of the existing display
need to be loaded from the server. Further, this system en-
ables a distributed server architecture as the client can ob-
tain tiles from a number of different servers. This greatly
increases the ability to distribute load across servers.
This method, however, imposes two primary limitations:
ﬁxed scales and static content. The tiles exist in a hierarchy
ofseverallayers, eachlayerrepresentsaﬁxedscaleatwhich
the map can be viewed. To enable caching the content of
these tiles must not change frequently, as each change re-
quires the tile to be re-rendered. The tiling scheme may also
be used without the cache, which would enable more dy-
namic content. However, rendering tiles is very CPU inten-
sive and building a scalable infrastructure to handle a large
volume of requests would be costly.
3. Methods
To overcome these limitations we propose a hybrid ap-
proach, in which map tiles are only partially cached. Our
approach, a Scalable Tile Map Service, enables a speciﬁc
category of maps to be served dynamically. These are maps
in which the geometric content remains static, but the in-
formational content changes, namely choropleth maps. Our
method for rendering choropleth maps over the web in real-
time essentially represents a new caching methododology.
Our approach has some features in common with the ap-
proach taken by [6], with several key differences; our ap-
proach doesn’t not render quantitative information into the
raster, the ﬁnal render is done on the server side, enabling
access by thin-clients such as mobile phones.
In the traditional Web Mapping Service (WMS) a classi-
ﬁcation is given in the initial speciﬁcation of the map. This
classiﬁcation generally speciﬁes the number of classes, a
range of values and associated color for each class and the
ﬁeld containing the value. The rendered map is essentially
a raster, with a limited number of values (the number of
classes + background + border). The rendered image is
saved into a common image format such as JPEG, PNG, or
GIF. The PNG (Portable Network Graphics) speciﬁcation
allows the raster to be separated from the color speciﬁca-
tion. The reasoning for this is to reduce ﬁle size. A 24-bit
color is comprised of a red, green and blue value of 8-bits
each. A type tile size is 256x256 pixels. Storing 24-bits at
each location would require 256x256x24 bits of storage.
Alternatively, anindexedPNGstoresthecolorsinacolor
palette. Each color is given a unique index and the index
is stored in the raster. One 8-bit value can represent 256different indexed colors, reducing the size of the raster to
256x256x8 bits. One can easily rewrite to the color palette
to recolor the map. However, the rendered map remains tied
to one classiﬁcation. At this point one cannot move region
A from class 1 to class 5 but one could change the color of
class 1 from blue to green.
Expanding on this, we remove this initial classiﬁcation
from the map speciﬁcation and instead render each region’s
unique ID into an integer raster. This integer raster repre-
sents the geometry of our map at a particular scale. Ren-
dering this geometry is a CPU intensive process. Using the
aforementioned tiling schemes we can cache these integer
rasters into a ”tile set.” Each tile in the tile set is identi-
ﬁed with an X coordinate, Y coordinate, and zoom level.
In the case of a choropleth map this tile set will only need
to change if the underlying geometry changes. The integer
rasters are highly compressible and do not require any more
storage space than a fully rendered tile.
The logical next step is to reclassify these integer rasters
in a meaningful way. To accomplish this we redeﬁne our
classiﬁcation speciﬁcation from ranges and associated col-
ors to a mapping from region ID to class ID. A second map-
ping from class ID to color is used to enable frequent re-
coloring of the map. This classiﬁcation system requires rel-
atively little storage space. When the map is derived from
an interactive process this classiﬁcation only needs to be
stored for a short period of time. This abstraction of the
map classiﬁcation from the map rendering allows any exter-
nal process with knowledge of the application programming
interface (API) to submit new classiﬁcations to the server.
The parameters necessary to visualize a map are
encoded in the map URL, the base level (follow-
ing the host) is the tile set ID, followed by the
classiﬁcation ID and color set ID. For example,
”http://example.com/TileSet/MapID/ColorSet” is the
URL for a map using the ”TileSet” tile site, the ”MapID”
classiﬁcation and the ”ColorSet” color scheme. Finally, a
tile ID, in the form of ”X+Y+Z.png” is appended to the
end of the URL. This request is handled by a light weight
”TileEngine” that implements the ﬁnal render of each tile
before it is sent to the client.
The TileEngine reads in the integer raster, classiﬁcation
and color scheme, as speciﬁed in the URL. The integer
raster is ﬁltered through the region ID to the class ID map.
The color scheme is saved to the palette portion of the PNG.
Theprocessdoesaddsomeadditionalprocessingtimewhen
comparedtoasimpletilemapservice, whichmerelyreturns
a fully rendered image. However, the overhead is negligible
compared to network transfer speeds. Further, each request
is handled by a separate process and in our implementation
these processes are distributed using Google’s AppEngine.
This allows the system to scale to a very large number of
requests.
Figure 1. Dynamic Web Mapping Service
Figure 1 illustrates the layout of our implementation.
The initial render of our map takes place in a traditional
batch job cluster or on a local computer system. The par-
tially rendered tiles are loaded into the cloud and stored in
Google’s BigTable distributed storage system. In more dy-
namic environments a classiﬁcation request will send pa-
rameters to the Map Classiﬁer, which loads relevant quanti-
tative data from the distributed storage system and saves the
classiﬁcation for future use. When a name is provided with
the incoming parameters the classiﬁcation will be stored in-
deﬁnitelyunderthatname. Whenanameisnotprovidedthe
map classiﬁer returns a temporary name which is valid for
24 hours. Any client capable of displaying a Tiled WMS
layer can not request the tiles from the cloud. Our imple-
mentation uses the Google Map API, which allows us to
overlay our tiles onto existing imagery and street networks.
The Map Classiﬁer and Map Tile Engine are imple-
mented as separate web services, each independent of the
other. This allows classiﬁcation to take on more complex
forms and/or be removed from the cloud and brought to the
local computing environment. In the empirical example that
follows a custom classiﬁcation module is designed to en-
able tight integration with a simulation toolkit designed to
examine water planning in the face of climate uncertainty
and other factors.
4 Empirical Illustration
We implement our approach in a proof-of-concept case
study in collaboration with the Decision Center for a Desert
City (DCDC) at Arizona State University. The objective
of the case study is to dynamically integrate results from
DCDC’s water simulation model WaterSim with our Scal-
able Tile Map Service. Watersim dynamically simulateswater supply and demand in the Phoenix metropolitan area.
It allows decision makers and stakeholders to adjust policy
levers and climate scenarios in order to simulate possible
outcomes. For the spatial layout of these simulations to be
of interest to the audience, they must be available in real-
time.
WaterSim leverages our dynamic web mapping service
to display its simulation results in choropleth map format as
the results are estimated interactively in real time for sub-
regions of the metropolitan area. We designed a classiﬁca-
tion module which serves as the link between the simula-
tion module and our map service. The classiﬁcation mod-
ule takes three inputs from the simulation results. These are
Phoenix metropolitan area’s share of the Central Arizona
Project, the Salt River Project and year-to-estimate popula-
tion.
These values are used with other pre-computed values
to calculate the amount of ground water that each provider
will need to mine in order to satisfy residential demand after
their surface water allotments have been used. This classiﬁ-
cation module also runs in the cloud (Google’s AppEngine)
and is accessible as a web service. The module submits the
classiﬁcations scheme to our map service and then uses the
Google Map API to overlay the new tiles on a Google Map.
Figure 2. WaterSim Example
5. Conclusion
This paper advances the dynamic rendering of choro-
pleth maps through a new caching method that is faster, ap-
plicable to more areal units, and scalable to larger numbers
of users than existing map based services. These improve-
ments are particularly relevant in applications where speed
and real-time interaction is of the essence. For instance, as
is the case in emergency response management, interactive
and real-time display of simulation results in map format,
and web based spatial exploratory data analysis. Our Scal-
able Tile Map Service lays the foundation for further exten-
sions in all of these and other areas. For instance, the Water-
sim case illustrated how a spatial dimension can be added
to traditional non-spatial simulation scenarios, which opens
up opportunities to reconceptualize interactive simulation
modeling from a spatial perspective. Our approach can be
used to extend the idea of visualizing raw data to analytic
results, such as outlier maps or residual maps. Extensions
of this work involve integrating our approach with ongoing
research in spatial web services to facilitate interactive web
based spatial data exploration and modeling [4].
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A Scalable Tile Map Service for Distributing Dynamic Choropleth Maps

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A Scalable Tile Map Service for Distributing Dynamic Choropleth Maps

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