Some researchers in the Computer Science department (nary a journalists to be found) are doing some interesting work trying to determine the degree to which people in different regions talk about different things.  The assumption is, obviously, that newspapers are a good proxy for what people are talking about.  Still, some smart people are wrestling with interesting ways to understand journalism.  Be sure to check out the published paper at http://www.algorithm.cs.sunysb.edu/mehler/mypapers/heatmap.pdf

spatial analysis of news sources

21 December 2006

We're not wild about using “image” as a verb, but the conference looks promising and certainly appropriate for those journalists who understand that we have to learn to tell stories with more than just words and pictures.  Yup, “HCI” is where it's at, or where it's going to be at.

Imaging the City:

Recent
technological developments mark the city as a central and perhaps
special space for human-computer interaction research and practice.
Visions of ubiquitous computing, the resonance of the ‘urban probe’,
and the proliferation of interactive mapping services speak to the
significance of the urban landscape to studies of Human-Computer
Interaction. But such visions and technologies require, produce and
reproduce images of urban space that influence what these systems, and
our interactions with them, are and might be. Developing and employing
technologies for the urban environment requires visualization
techniques that both reflect and challenge how we image, and
consequently imagine, the city.

• How do we represent the city in HCI, and how do these representations inform HCI research and practice?
• What
  kinds of technological devices, services, and platforms support imaging
  the city now and might be created in the near future?
• How are and might these new representations of the city and urban imaging technologies be used for social and political ends?
• What new methods are required for developing technologies that image the city in new ways?
• What can we learn from the urban experience to design stronger representations and interfaces within HCI research and practice?

The Rrove blog — no, no, not THAT Rove (different spelling) — delivers a round-up review of nine sites related to community mapping tools.  See http://www.rrove.com/blog/2006/12/04/9-awesome-community-mapping-websites/

Disclosure: Rrove.com
plays in the community mapping space. This post aims to highlight the
innovations and the usefulness that others have made in this game. We
haven’t added ourselves to this list – if you want to know more about
Rrove, click here.

A
community mapping website, in our definition, is a service that gets
its members to map and define places. Through crowd-sourcing, these
sites are building a database/directory of local and nearby locations
that their users can discover and visit. Why is this important? We all
know that search advertising is the fastest growing industry in the
Internet. Within that market, local search is the up-and-comer. In the
next few years, it will be the largest segment within search!

It’s
refreshing to see how others have approached community mapping. Some
have focused on map creation while others do it through mobile apps.
More than that, some players have mapped the community of users to map
the physical community (i.e. neighborhoods). Here’s how nine websites
(all free) are doing it, what makes them awesome and how you can use
their services in your Internet life.

It was the second year of the national crime mapping conference when we realized that, hey, there's a lot of not-just-good-but-great analytic work going in the then-young profession of crime analysis.  Seven years later, it's just getting more impressive. 

If you can only get to one national conference a year (we assume you're already going to the NICAR meetings), do this one every other year and the Special Libraries Association convention on the off year.  NOTE: NO NO NO registration fee!

Registration for the Ninth Crime Mapping Research Conference has opened.  This year, there will be no conference
registration fees but registration is still required.  Preliminary conference details available on the
MAPS website: http://www.ojp.usdoj.gov/nij/maps/pittsburgh2007/index.html

The
Ninth Crime Mapping Research Conference will take place March 28-31, 2007 at the
Omni William Penn Hotel in downtown Pittsburgh, Pennsylvania.

Agenda:

The conference will include a full
compliment of workshops, panels and plenary sessions. The main plenary session is entitled GPS in a Crime Analysis Context-
Practitioner Consideration, Research Needs.” Panel
session topics will include uses of spatial data analysis and GIS in
corrections, parole, and probation, geography and crime, geographic profiling,
offender travel behavior, NIBRS/incident-based data and mapping, international
programs, impact of Hurricane Katrina on crime, crime analysis, spatial data
analysis, policing issues, managing sex offenders, travel demand modeling, and
more.  The conference also includes a map competition, and provides an
excellent opportunity for researches and practitioners to network with each
other.

The folks at Faneuil Media, a company that “helps site owners publish maps and data, just announced a promising tool, “Atlas.”  Check it out because they say….

Meet Atlas

Thanks to Gary Price at ResourceShelf Newsletter <http://www.resourceshelf.com> for this:

Think we might be able to rent the roof of our homes or offices as virtual ad space?  From the All-points Blog:

Virtual Earth 3D Futures

Friend and mega-librarian Marylain Block's “Neat New Stuff” column (http://marylaine.com/neatnew.html) points us to another example of a great community-building tool.  She writes:

A former student of colleague Steve Ross sends this interesting report on how simulation models can/are being used in the real world:

I’m the communications officer for the International Research
Institute for Climate and Society at Columbia University. The IRI specializes
in making forecasts of climate for every part of the world by using data from
satellites, meteorological stations and proxy records (tree rings, corals, etc)
to run models. The models tell us, with varying degrees of certainty, how much
off the “norm” rainfall, temperature and humidity will be for a given place in
the world.  We’re not so much interested
in long-term climate change (global warming, sea level rise, etc) as we are in
season-to-season changes (e.g. monsoonal patterns, drought, flooding, etc.).
And the IRI isn’t a purely academic institution — its main objective is to use
the forecasts and climate monitoring to develop and undertake projects that
mitigate the effects of climate change in developing countries. We never do
this alone: all of our efforts are in collaboration with scientists, policymakers
and NGOs in these countries.

Here’s a brief description of some projects we’re currently working
on:

*Climate and malaria:*
The IRI collects an enormous amount
of temperature, rainfall and humidity data for southern Africa. As it it turns
out, the presence or absence of malaria in a given region depends strongly on
these three climate factors, so scientists here developed a mapping tool that
shows the risk of a malaria epidemic for every month of the year in every part
of sub-Saharan Africa. We train health workers from countries in this area on
how to use the information to adequately prepare for epidemics. (see /http://tinyurl.com/yxzp7t/
)

*Climate and fire-management*
Fires in Indonesia damage unique
and delicate ecosystems, increase carbon dioxide emissions, and produce noxious
smoke and haze that leads to thousands of hospitalizations every year. Since
the intensity and duration of these fires depend on the amount of rainfall the region
receives every season, the IRI is using its rainfall forecasts to develop an early-warning
system that policymakers and NGOs can use for planning purposes. For example, if
our models tell us there is a strong chance of drought conditions in the next 3-month
period there, our Indonesian partners can take specific actions, such as conserving irrigation water so that the fields where these fires
occur aren’t drained completely and therefore aren’t as susceptible to burning.
(see /http://tinyurl.com/yjehn6/)

*Index-based weather insurance for farmers*
The IRI and the Commodity Risk Management Group at the World
Bank are involved in a project to develop insurance contracts that protect Malawi
farmers against periodic, crop-destroying droughts. Traditionally, farmers would
take out loans to buy seeds at the start of every season. If a drought occurred,
the farmers’ crops would die, and they wouldn’t be able to pay back the banks. But
under this new program the farmers can purchase an insurance (a very small percentage
of the price of the seeds) against crop loss when they buy seeds. If a drought occurs,
the farmers get a full or partial payout and can use the money to repay their loans.
IRI’s role in this is to use its weather monitoring data for the region to help
the local insurance companies develop reliable contracts. The fascinating aspect
of this program is that it is completely subsidized by the farmers.  (no link
available yet)

My role in all this is to make these and other projects known
to the public at large.  Many of you receiving this email are journalists of
one species or another. If you are developing stories or graphics that have to do
with climate or earth science, think of me. I’ll put you in touch with experts or
send you bucketfuls of GIS and other data.

Francesco Fiondella
Communications Officer
International Research Institute for Climate and Society
Lamont-Doherty Earth Observatory
61 Route 9W
Palisades, NY 10964
francesco [at] iri.columbia.edu
1.845.680.4476

Mike Stucka stucka@whitedoggies.com  today provided a rich set of links during a discussion of mapping on NICAR-L.  If you're interested in Minard's map/chart/info graphic of Napeoleon's march to Russia — and especially if you use it as a teaching tool — check out the links below.

Click here to see the SAS code (zoomed-in view)

Charles Minard is a famous map-creator from the late 1800's.
Perhaps his most famous map is the following showing 
Napoleon's March on Moscow in 1812.

I looked at several other people's version of Minard's map 
on the following webpage and devised an idea for my own 
version, using SAS/Graph's "proc gmap".

I used longitude/latitude, troop number, and city data from the following webpage
in combination with the temperature data from the following translation of Minard's Map.

I'm somewhat "geographically challenged" when it comes to europe, especially
with the city names on the Minard map (and I'm probalby not alone?)  
Therefore I decided to plot Napoleon's path on *modern* maps, with the
country names labeled.  Also, I wanted to give a "50,000-mile" view of
the map, so people could see where this area was in relation to other
areas of europe (such as France).

*****************************************************************
*
*  Here's my 50,000-mile map.
*
*  Which drills-down to this Zoomed-in map.
*
*  Here is the SAS code used to generate the maps.
*****************************************************************

Here are some details about how I created my map(s) in SAS/Graph...

For both of these maps, I used a combination of the sas maps.europe 
and maps.asia as the base map.  I created custom/subsetted/clipped
versions of these maps using SAS' "proc gproject", and specifying
the latmax/latmin/longmax/longmin for the area I wanted in my map.

To get the blue ocean/sea water to appear only in the map area I use
an annotated rectangular polygon with corners at the exact same
long/lat coordinates as the corners of my map, and annotate it 
'behind' the map.  (If I had used "goptions cback=blue" that would
have filled the entire background of the page with blue, not just
the wanter area in the map.)

In the 50,000-mile map, I annotate country names at long/lat
positions of my choosing, and I annotate a dashed-line box around
the area that will be shown for the drilldown.  In this dashed-box
area, I use annotate's "html" variable to encode a drilldown, so 
that when you click inside this rectangle it drills-down to the 
zoomed-in map.  I also annotate a dot at the city locations (and
I annotate city labels in the zoomed-in map).

I use the same technique in both maps to show Napoleon's path.
I take the long/lat values, and use them as vertices for a line,
and connect the dots with line segments (using annotate move/draw
functions).  The size/width of the line is calculated based on the 
number of troops (ie, men) still alive during that leg of the trip.
One problem with this technique was that it produced big/jagged
gaps/transitions at the line vertices (especially where the lines
changed directions sharply).  To smoothe this out, I annotated a 
'pie' (filled circle) at each vertex, with the diameter of the 
pie being the same as the width of the line.  With this annotation
(as with most all of the lat/long-based annotation in this map)
I combine the annotation with the map, use "proc gproject" to do
the map projection, and then separate the annotate from the map
(this guarantees that things line up in the correct position).

On the zoomed-in map, notice that when you hover your mouse over the 
city names, you see the city name (and additional info, if available)
for that city.  If you click on the city names, that launches a 
google search for information about that city (including the words
'Napoleon' and '1812' in the search).

When you hover your mouse over the vertex points of Napoleon's path,
you'll get a html charttip/flyover-text showing the number of troops
at that point during the march.  At first I made these hotspots the
exact same size as the visual vertex dots, but the small ones were 
too small -- therfore I annotate a larger dot 'behind' the map for 
each vertex, and the charttip is based on the size of those 'invisible'
vertex points :)

Surprisingly the toughest part of the map was the temperature plot.
After trying a few different approaches, I decided to base the position
of the temperature dots and the axes & gridlines & labels all using 
long/lat coordinates.  The distance from the minimum gridline to the
maximum gridline is 1 degree of latitude (not to be confused with 
degrees celcius or degrees farenheit! ;).  And for the horizontal 
positions, I use he position of the cities/battles corresponding to
the temperatures.  I color the temperature dots an 'ice cold' light
blue, since they represent cold temperatures.

/* Written by Robert Allison (Robert.Allison@sas.com) */