So who is ready for the switch and who isn’t? We made a few maps based on the Neilson report showing who was ready and who will be left behind in analog. Check the maps out below.

Click on the Eye Icon to turn layers on and off.

Maker.maker_host=’http://maker.geocommons.com’;Maker.finder_host=’http://finder.geocommons.com’;Maker.core_host=’http://core.geocommons.com’;
Maker.load_map(“maker_map_5993″, “5993″);

To download the data or view the meta data visit Finder here!

To help us, stimuluswatch.org has set up a site dedicated to helping “the new administration keep its pledge to invest stimulus money smartly, and to hold public officials to account for the taxpayer money they spend.” They provide a database of “proposed ‘shovel-ready’ projects” throughout the country which will be candidates for federal grant money as part of the stimulus package. The site offers the capability for citizens to view the proposals and decide if they think they are critical or not.

In order to help viewers better assess the appropriateness of these projects, we uploaded the data to Finder! and then used Maker! to compare where these projects will be and where jobs are most needed.

In the map below, we show the projects by the number of jobs that will be created. The larger circles are where more jobs will be created. We also show the change in unemployment by county between November of 2007 and November of 2008. The blue counties are where there was a decrease in unemployment, the white where there was a fairly small increase, and the yellow and orange areas show larger increases.

Taking a look at the country as a whole, it does seem that many of the projects are proposed in areas that have suffered job losses. This is particularly true for areas of Southern California, Florida and the Rust Belt. Areas in the center of the country, where there have been the some decreases in unemployment have less proposals for job creating projects.

Lets look more closely into an area to examine how the proposed projects are matching up to job losses. Georgia is one area that seems to have experienced a heavy loss in jobs over the past year.

You can see in the map above that there are many clusters of counties whose unemployment rate has increased by more than five percent in Georgia. None of these counties have a project planned in the direct vicinity. The county of Hancock Georgia has had the highest increase in unemployment and the third highest unemployment rate for this November of all the counties in the US. In November of 2007, its unemployment rate was 9.2 and in November of 2008 the rate reached 20.1, a 10.9 percent increase overall. The nearest proposed projects to Hancock are either an hour and a half away in Macon or an hour and forty minutes away in Conyers.

While the governor of Georgia may have good reasons for creating jobs in the proposed areas, it leaves one to wonder what will become of the towns, such as Hancock, who have suffered the greatest in this economic crisis.

Take a look at this map yourself in Maker!. You can zoom in to areas you are interested and decide for yourself the validity of these projects.

On the other hand, it is interesting that Illinois is fairly well represented here. Of the 891 projects in the country, 119 or 13.8% of them are in Illinois. While Illinois does have some yellow and orange counties, it is by no means the hardest hit state in the country in terms of unemployment. Does the state expect some favoritism from the new president?

At a closer look, the 119 projects in Illinois will create significantly fewer jobs then projects in other states. California, which faced the fourth highest unemployment rate in November, is proposing 93 projects which will produce 238,329 jobs.

The chart below provides 16 states with the highest unemployment rates in November along with the number of projects proposed in each state and the total number of jobs and the number of jobs per 1,000 people those projects will create.

States like Michigan and South Carolina, who need jobs the most are proposing projects that will create comparatively few jobs per capita. You can download a CSV of this dataset from Finder! and do your own analysis of the proposed projects.

We can also look at the projects compared to state unemployment rates, as is seen in the map below. The yellow and orange states are the ones shown in the graph above. To see this map click here.

Of course nobody is saying that the unemployment rates should be the only criteria as to where stimulus money should go. But if the package it going to truly address unemployment, projects that will add significant jobs to areas with high unemployment rates should be considered strong candidates for federal funding.

You can read Obama’s technology platform overview here. The plank that really grabbed my attention was the promise to “Open Up Government to its Citizens”. The idea that data about government (Congressional voting records) and created by the government (census data) should be easily available to the public. Specifically:

“Making government data available online in universally accessible formats to allow citizens to make use of that data to comment, derive value, and take action in their own communities. Greater access to environmental data, for example, will help citizens learn about pollution in their communities, provide information about local conditions back to government and empower people to protect themselves.”

The beauty is that we (the collective GeoWeb) have so many of these tools already built. The ability to deliver the data once it is made easily available has great promise. For instance here is EPA data on power plant emissions from GeoCommons:

From the map above you can see which power plants are producing the most poisonous CO2 emissions (click the down carrot on the layers box for the filter) or zoom into your specific neighborhood to see the plant and the type of environment around it. (Still refining the embed capability, but an example of how data can be virally spread).

The report goes on to recommend that the federal government should:

“Establishing pilot programs to open up government decision-making and involve the public in the work of agencies, not simply by soliciting opinions, but by tapping into the vast and distributed expertise of the American citizenry to help government make more informed decisions.”

This strikes again at the heart of the GeoWeb – enabling collaboration of experts and citizens across the country. Several projects and companies have pioneered dynamic collaboration around maps. Below is a Google MyMap with feedback around the GeoCommons power plant data in Florida”

View Larger Map

The blue push pins are the user generated feedback linking to expert opinion and photos from the field. This is just the tip of the iceberg of what is possible with collaboration around maps. These approaches can also be leveraged inside of government agencies, which is another plank in the Obama technology platform:

“Employing technologies, including blogs, wikis and social networking tools, to modernize internal, cross-agency, and public communication and information sharing to improve government decision making.”

We’ve seen a lot of this type of work going on in the intelligence community with Intelink, Intellipedia, and A-Space. There is also data fusion and sharing concepts, like the EPA’s Central Data Exchange. I’d love to hear other projects that fit in with the three planks, and more importantly existing or planned GeoWeb technologies that could help enable the new vision. I’ve really only highlighted two and I know there are tons more out there.

Raj found a Chinese government source for daily pollution levels in Beijing then geocoded it for us. Andrew used Mapufacture to turn the data into a dynamic geocoded feed, so we could put it on the map and have it updated in real time. Finally Bill used Finder to create a data set with all the Olympic venues. The whole thing came together in a day and we’ve embedded the map below. You can also go to Mapufacture and grab the embed for yourself or the KML and keep track of it in Google Earth or another compliant GeoBrowser.

The icons with the Olympic rings are Olympic venues and the orange squares with the Mandarin character for “atmosphere” are pollution sensors. When you click on the environmental data you’ll see totals for three different pollutants:

1) SO2 – Sulfur Dioxide
2) PM10 – Particulate Matter- that is 10 micrometers in diameter or less
3) NO2 – Nitrogen Dioxide

These three measurement compose the API – the air pollution index. The USA and Canada both use AQI, air quality index, which is similar to API but the indices are set according to different formulas based upon concentration. Therefore, one cannot compare API or AQI between countries without knowing what concentration is represented by the indices.

API Rating (Beijing)

0-50 Grade I (Excellent)

51-100 Grade II (Good)

101-200 Grade III (Lightly Polluted)

201-300 Grade IV (Moderately Polluted)

300+ Grade V (Seriously Polluted)

Interestingly Honk Kong uses the same API index but how they rank between “seriously polluted” and “excellent” is far different.

To keep things honest we used the Honk Kong scale for the map we created. If you would like to do some comparisons (knowing the formulas) of API in Beijing to AQI in other cities you can check out the EPA data set in Finder with AQI for major urban counties in the USA.

*Special thanks to Andrew’s wife Corrie for deciphering the enviro science for us. If you would like to get the PhD. version check out her blog on the topic here.

In addition to working with great people we also saw great potential in blending our technologies. Mapufacture has done a phenomenal job pushing open standards on the GeoWeb and making dynamic data available on maps. In many ways they embodied the core of what many have defined as neogeography. At F1 we’ve had a mission of bridging the GeoWeb and GIS, and have invested lots of effort in bringing the vast world of GIS data to the GeoWeb and the public at large.

The long term vision has been to eventually fuse the personal and dynamic data of the GeoWeb (long tail) with the static and statistical data of GIS (short tail). Mapufacture presented us the opportunity to accelerate that vision and bring a broader vision of geospatial awareness to the public. The chance to bring dynamic and static data together in an intuitive way. The opportunity to combine quantitative data with qualitative user generated feedback.

Imagine a world where it is not only possible to look at toxic release locations by volume of discharge, but also have the citizenry comment with words, photo and video on the impact it is having on their community. What if an accident happened at a toxic release point? You can now bring in real time weather and see if wind dispersal could be a hazard for response. The examples are innumerable. It could be your sales data dynamically generating from Salesforce.com and combined with detailed demographic data to better coordinate your next marketing campaign.

There is obviously a lot of work to be done, but the Mapufacture and FortiusOne teams are both excited to bring the potential of what the GeoWeb can be to the market. We are excited and look forward to showing the first step in that vision later this month. In the mean time we’d love to get your thoughts on possible new logos for Geo-Voltron.

Candidate A

Candidate B

So, if there indeed are Bush donors who now have become Obamacans, the data-team wanted to find out where they are spatially speaking. Below are the maps of our efforts showing locations of possible Obamacans in New York City and Washington D.C. Why use the term possible? Because what is mapped are the results based on spatial join and attribute join, the later being a variation of spatial join. And the accuracy of the results of such joins is subject to the limitations imposed by the accuracy of the original data (donor addresses) as well as limitations of the geocoding operation. More on this towards the end of this post. So what is mapped are donor address matches and not individual donors.

Attribute Join
The attribute join is based on an identifier “XY” constructed from the concatenation of X and Y location coordinates of the Bush-Cheney and Obama donors, where the X and Y location coordinates are obtained by geocoding donor addresses. The attribute join resulted in 250 records across the lower 48 states, mostly concentrated in major cities of North-East and West-Coast. The results are shown below for New York city (lower Manhattan) and Wash D.C., where blue circles represent Obama donors (1,415 in D.C. and 1,825 in New York city); red circles represent Bush-Cheney donors (294 in D.C. and 419 in New York). The purple squares colocated with Bush-Cheney red circles are the XY “attribute matches.” There were 32 such locations in D.C and New York City had 85.

New York City: “XY” attribute join of Bush-Cheney donors with Obama donors

Washington D.C.: “XY” attribute join of Bush-Cheney donors with Obama donors

Spatial Join
Yet another way was to carry out a “spatial” join between location of each Bush-Cheney donor with all of the co-located Obama donors, resulting in more than 9,200 Bush-Cheney records colocated with more than 42,000 Obama records in the lower 48 states. The results are shown below for New York City (lower Manhattan) and Wash D.C., where again blue circles represent Obama donors, red circles represent Bush-Cheney donors, and the purple circles with varying sizes represent count of Obama donors that are colocated with each of the “spatially” joined Bush-Cheney donor. There were more than 1,500 Obama donors colocating with 248 Bush-Cheney donors in D.C. while the comparable figures for NY city are more than 2,030 Obama donors colocating with 303 Bush-Cheney donors.

Bush-Cheney donor locations spatially joined with Obama donors in NY City

Bush-Cheney donor locations spatially joined with Obama donors in Wash D.C.

Donor Data
You may find/download the mapped as well as other supporting datasets from the Finder! by using the key-word “Obamacans“. The supporting datasets also include spatial join of all Bush-Cheney donors for each of the Obama donors.

A strict one-to-one Name/Address match between the Bush-Cheney and Obama donors based on uniquely generated ID to identify the real Obamacans resulted in zero matches. Unique IDs were constructed by concatenating the upper case fields of each donor record: “LAST NAME”, “FIRST NAME”, “STREET ADDRESS”, “CITY”, “STATE” and “ZIPCODE”. See below for the explanation of such disappointing results.

Geocoding, Mapping “join” and the results
Individual Bush and Obama donor lists were compiled from the publicly available donor records from the campaign finance reports filed by all presidential candidates. Federal Election Commission (FEC) rules permit an individual donor to contribute more than once with the condition that the total of all such contributions may not exceed $2300 per election cycle (primaries and general). Since both Bush-Cheney and Obama donor list include such multiple donation records, the resulting geocoded data inherits the many donor-multiple records problem.

The geocoding success rate depends not only on the accuracy of the address information, but if street address is not provided, then that donor’s location will be geocoded to the centroid of the zipcode/city/state areas mentioned in the record. Hence, we decided to limit our analysis to only those geocoded records that had street address and with the geocoding success score of 90 or more out of max possible score of 100. Next, multiple records per individual were eliminated by uploading the data to Access database, creating a unique ID based on donor’s name/address and by running sqls to find match between Bush and Obama donors based on the unique key. As was stated earlier, the output showed zero matches. Some of the possible reasons for zero matches are: donor names are spelled differently at different times, address changes over 4 year time period, some donor addresses have just PO boxes or only zipcode/city/state information; and/or combinations of these, which results in poor geocoding output.

The difference in number of matches with the “XY” attribute join and spatial join can be explained as follows: in case of the former, there may be more than one record for each of Bush-Cheney record, however, attribute join is between a Bush-Cheney record and the 1st of the multiple Obama records, while in the case of later, the multiple join occurs between spatially adjacent records of both Obama and Bush-Cheney. Multiple records with same location address often occur in cities where geocoding cannot distinguish between a multi-storied location from single family homes or where many donors live on the same city block(s).

For the above analysis, nearly 150 K Bush-Cheney individual donor records were extracted from the campaign finance reports filed with FEC between Oct, 03 and Sep, 04. Similarly, more than 560 K Obama individual donor records were extracted from the monthly campaign finance reports filed with FEC between Jan, 08 and Apr, 08. After geocoding both the Bush-Cheney and Obama donor records, only those with a geocoding score of 90 and more out of max of 100 were selected, further all those records that were geocoded to the centroid of zipcode or city/state were deleted. Additionally all those multiple records of many donors were aggregated to just single record, making the final tally for Bush-Cheney and Obama to be 15,742 and 38,042 donor records for the lower 48 states. “Spatial join” and “XY” attribute join between Bush-Cheney and Obama yielded a little over 9,270 and 250 records respectively.

So what is shown in the maps are address matches between Bush-Cheney and Obama donors rather than the donors themselves. As is evident from many of the articles in the MSM, there may indeed exist a few Obamacans but our analysis of FEC campaign finance reports fails to name the names. I would love to hear suggestions on how to improve on these results or better yet, a methodology to spatially identify/locate the Obamacans.

The Cuban government provides in depth statistics on the health of its population by province and finder has these data available for the years 1996 to 2006 with more than 80 health and health care related attributes. Whether you are interested in the change in infant mortality over the last decade, which provinces have more doctors per resident, or what is the leading cause of death in each province, this dataset will help illustrate what the situation is on the island.

Here is an example of what these data can be used for. This map shows the number of family doctors per habitant in 2006. Provinces in red have less doctors and the green ones have more.

See data for:
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996