Some Data and Sources for 387 Execises

worldpopgdppts.txt This (dos) cvs file contains ostensible point data: place name, longitude, latitude (rough coordinates from a mixture of sources), population (2013, UN estimate, in thousands), and GDP (2010, UN esitmate, US$). Missing data are coded with -99. The 211 records are unlikely to exactly match anyone's list of countries.

NASA's Socioeconomic Data and Applications Center (SEDAC) Numerous datasets organized by theme.

Natural Earth - cartographically useful treatment of physical and cultural features.

GSHHG - Paul Wessel's (UHM SOEST) Global Self-consistent, Hierarchical, High-resolution Geography Database. Derived from the CIA WDBII and the NOAA World Vector Shoreline. Available in several formats and five resolutions.

NOAA National Climate Data Center (or link to data products ) - several climate data sets.

Drifter buoy datasets - these are space-time-attribute data sets; buoys drifting with ocean currents and reporting location, time, sea surface temperature, wind conditions etc. Organized by regions.

UN FAO (Food and Agriculture Ogranization) here

UNICEF here . Look under "Customized Statistical Tables".

HI OP GIS Data here

C&C of Honolulu GIS Data here

Pennsylvania Oil and Gas Well data sets

Ebola... CDC distribution map Finer grained data might be better.

1912 Transcontinental datasets.

Some classic data sets from the geostatistics literature... here . Most are soil chemistry data with several variables for each site.

2014 ACT scores - a local news source pointed out that we can map Hawaii vs other states on several dimensions based on the data found on page 14. Ah the joys of data via .pdf!

CO2 concentrations extracted from NOAA data: world-co2-data.txt and a more Arc-friendly version... world-co2-data2.txt with DOS CR LF, pairs, a header line of quoted fieldnames, and "," field seperators. See, for context for this data. Drill down under the "products" tab form to see the data archive for the CO2 flask data. The last record for each of the 'event' files was extracted. The twenty-five data fields are documented on-line here . Key fields you might find useful are:

Field 2:    [YEAR] The sample collection date and time in UTC.
Field 3:    [MONTH]
Field 4:    [DAY]
Field 5:    [HOUR]
Field 6:    [MINUTE]
Field 7:    [SECOND]
Field 12:   [MEASURED VALUE] Dry air mole fraction or isotopic
             Missing values are denoted by -999.99[9].
Field 13:   [ESTIMATED UNCERTAINTY] Estimated uncertainty of the
             measurement value.  Missing values are -999.99[9].
Field 22:   [LATITUDE] The latitude where the sample was collected, 
            (negative (-) numbers indicate southern hemipshere).
Field 23:   [LONGITUDE] The longitude where the sample was collected, 
             (negative (-) numbers western hemisphere).
Field 24:   [ALTITUDE] The altitude where the sample was collected (masl).

Hawaii Census Block data from PL94-171

The latitude and longitude of the interior point (INTPNT), population, housing unit count, area water, and area land for of each of Hawaii's 25,016 census blocks is in 2010censusblockpts.txt. You will eventually notice that this includes unpopulated 'blocks' in the far reaches of the archipelago and may want to exclude longitudes west of Niiahu (i.e., longitude < -160.25).

Another file 2010blk-pop-counts gives the number of blocks with various populations. E.g., there are 13,741 blocks with no population (0), and 1 block with 3,241 people. This might be handy if you want to anticipate how to range grade the data.

Some made-up data

Hypothetical plume data as ASCII text in AtlasGIS ".bna" format generated with this python script, which clearly could be improved to produce more realistic output.

Two more made-up space time data sets to mess with Tracking Analyst. Here are 24 hours (one hour steps) of a plume a plume expanding in kml format , in Arc's old "generate" format (with an associated text file to hook times to plume polygons here), and in the old AtlasGIS ".bna" format, and the same 24 hours of ten critters running around randomly at different rates, near the plume, recorded at one minute resolution. (Does the difference in time resolution cause trouble?)

BTW, Here is a python script to generate kml polygon data like that: . It seems that kml is the new gen-file.

As requested in class, here is that outfile.kml that had the problems with (1) no enclosing tags and (2) spelling them with lowercase "d". Interesting that ArcGIS imported them anyway. Experiment away!

USGS Earthquake Data real-time feeds . The spreadsheet formatted data may be easier to use than the other options.