"Ben Schmidt, assistant professor of history at Northeastern University, has visualized the routes of 19th Century ships using publicly available data set from NOAA (National Oceanic and Atmospheric Administration). The resulting image is a hauntingly beautiful image that outlines the continents and highlights the trade winds. It shows major ports, and even makes a strong visual case for the need for the Panama and Suez Canals."
The Brazilian government's geographic department (Instituto Brasileiro de Geografia e Estatística-roughly equivalent to the U.S. Census Bureau) has compiled an fantastic interactive world factbook (available in English and Spanish as well as Portuguese). The ease of navigation allows the user to conduct a specific search of simply explore demographic, economic, environmental and development data on any country in the world.
By moving the slider, the user can compare 1990 false-color Landsat views (left) with recent true-color imagery (right). Humans are increasingly transforming Earth’s surface—through direct activities such as farming, mining, and building, and indirectly by altering its climate.
This interactive feature includes 12 places that have experienced significant change since 1990. This is an user-friendly way to compare remote sensing images over time. Pictured above is the Aral Sea, which is and under-the-radar environmental catastrophe in Central Asia that has its roots in the Soviet era's (mis)management policies.
Tags: remote sensing, land use, environment, geospatial, environment modify, esri, unit 1 Geoprinciples, zbestofzbest.
Python has been more tightly integrated in the new release of ArcGIS 10, allowing scripting to occur directly through a Python process without even opening up ArcMap. Admittedly this was available before, but now everything is more tightly coupled and a lot cleaner in it’s implementation. However, what has really interested, and indeed confused me of late is how to use Python in the ‘field calculator’.
Field Calculator is a really useful tool, when you are looking at an attribute table for a shapefile in ArcGIS and you want to derive a value for each object in the file based on a function you can input the function into the field calculator and it will work it out for you row by row. Sometimes the value you want to derive is a bit more complicated than simple arithmetic and you need to write a script. Previously you could do this in VBA, but I always found it limited and confusing, now however you can do it in Python – much simpler!
There are a few pitfalls to using Python in ArcGIS field calculator, and so I’m going to specify how to write simple field calculator python scripts in ArcGIS from my early experience.
Firstly, for Python in field calculator the way to do it seems to be in write a Python function, and then call it for each row. In addition to this, because you are writing a function you have to give it the relevant parameters (i.e fields) with which to do the computation. Finally, and annoyingly you have to write your function in a little box, and use a consistent indentation standard (1 space works best for reasons of space) as Python requires.
Until now, there has been a lack of solid, comprehensive spatial data about African groundwater resources. Researchers have now done so. For a more academic article on the subject, here are their findings in Environmental Research Letters.
This is a clip from the TV show West Wing (Season 2-Episode 16) where cartography plays a key role in the plot. In this episode the fictitious (but still on Facebook) group named "the Organization of Cartographers for Social Justice" is campaigning to have the President officially endorse the Gall-Peters Projection in schools and denounce the Mercator projection. The argument being that children will grow up thinking some places are not as important because they are minimized by the map projection. While a bit comical, the cartographic debate is quite informative even if it was designed to appear as though the issue was trivial.
Questions to Ponder: Why do map projections matter? Is one global map projection inherently better than the rest?
When African states gained independence, the continent's new leaders agreed to respect the old colonial borders to avoid endless wars.
This interactive map shows the major conflicts on the African continent where the combatants have geopolitical aspirations to separate from the state and create a new, autonomous state. Click on the red arrows and you can read about the warring factions and the current situation in that region.
Tags: political, governance, Africa, unit 4 political, war, conflict, states, colonialism.
VENTURES AFRICA – Rapelang Rabana has been listed on Oprah’s 2012 ‘O’ Power List, mentioned by CNN and is a World Economic Forum Global Shaper, all before the age of 30. A founding partner of Yeigo Communications, developer of some of the earliest mobile phone VoIP applications, shared her experiences and insights with Ventures Woman in an interview. Here’s what she had to say:
API Cartographer Eric Fischer plots language shapefiles of Twitter.
Some other images show how social media cuts across place, time and culture and communications have 'defeated' geography to unite the world. This image (besides looking pretty) shows that culture and place still matter within our increasingly interconnected globalized communications. There are some very real creating obstacles to diffusion and even if the technology exists for "one huge conversation," there are non-intersecting conversations because of cultural and community differences.
When writing a research paper or an article that contains references to GIS data, maps, or other geospatial material, it's important to include a proper citation crediting the author of the GIS work.
Citations vary depending on if the map is a single piece of work, part of a map series, an atlas, or a map that is part of a book or a journal article. There are even specific citations if the map was created using GIS software or you are citing GIS data. There are varying citation guidelines for static web maps versus dynamic online mapping applications.
For each map, first consult the original work in order to extract the necessary information. Scan the map for the necessary information. If some of the needed citation information is not listed directly on the map, access any available background information. If the map is found within a book, article, or atlas, look for any figures or footnotes that provide additional detail. If the map is accessed from a web page, check for any background information on the source web site. Make sure you carefully note within your citation any missing information.......
The Gangnam Style! sensation is all over the internet, complete with parodies that both honor and mock the original. This first video is the original, which in a few short months received well ove...
The following link has the video, parodies and infographics to help student explore the meaning behind the cultural phenomenon.
Questions to Ponder: Considering the concept of cultural diffusion, what do we make of this phenomenon? What cultural combinations are seen in this? How has the technological innovations changed how cultures interact, spread and are replicated?
Tags: popular culture, video, diffusion, globalization, culture, place, technology, unit 3 culture.
Introduction Sampling design is a critical part of any study involving modeling and estimation based on data that is sampled from natural resources or other phenomena occurring in the landscape. Statistical considerations related to sampling are part of a larger scenario involving theoretical knowledge, previously detected behavior and patterns of the phenomenon, costs, accessibility to sample sites, politics, and so forth. Thus, the sampling design algorithm should be flexible enough to accommodate external considerations in the design. Currently, ArcGIS offers a number of different methods which include, Create Random Points, Create Random Raster, Create Spatially Balanced Points and Densify Sampling Network geoprocessing tools. Some of these methods can be used to design a new monitoring network and others can be used to add or remove monitoring sites from an existing monitoring network.
In this blog we’ll use the Densify Sampling Network geoprocessing tool to identify locations for new rainfall monitoring sites for an area on the east coast of South Africa. We want to find out where to place additional monitoring sites so that the mean annual precipitation surface created by interpolation can be improved. One can randomly suggest new locations in areas that are void of monitoring sites, however, these might not be locations that will yield a more reliable output prediction surface.
The Densify Sampling Network tool requires an existing monitoring network with measurements at known locations. Prior to running the Densify Sampling Network tool one needs to create a kriging geostatistical layer which can be done via the interactive Geostatistical Wizard or the Empirical Bayesian Kriging geoprocessing tool (new in ArcGIS 10.1).
For this example our new site selection criteria will be the standard error of prediction surface, associated with the kriging layer, which will be used to assist in determining where to place the new monitoring sites. An optional weight raster can also be used to give additional preference to the new site locations, in our case it’ll be given a value of one in areas where monitoring sites can be placed and zero’s in the ocean where we do not want to place monitoring sites. Simplistically, the following is done when the tool is executed. The standard error of prediction surface and the weight raster are combined and the location with the largest value is deemed to be the location of the new site. A prediction, using the kriging layer, is made at this location and this value is included in the input feature class of existing monitoring sites and a new standard error of prediction surface is generated. This surface is then again combined with the weight raster to decide where the next location should be. This sequential process is repeated until the desired number of new monitoring sites has been created. An inhibition distance can also be used to ensure that new monitoring sites are not too close to one another. If a proposed new site falls within this inhibition distance the location of the next largest value from the combined standard error of predictions surface and weight raster is selected......
The TopoToRaster tool is primarily used to create hydrologically correct digital elevation models (DEMs) from contour data. Additional input information such as spot heights, known locations of sinks, streams and the outline of lakes can also be used to further improve the quality of the output DEM.
ANUDEM is developed and maintained by the Fenner School of Environment & Society at the Australian National University. It calculates DEMs with sensible shape and drainage structure from a series of topographic data sets . TopoToRaster internally calls ANUDEM, which imposes some size constraints on the size of the output raster.
When TopoToRaster is run as a geoprocessing tool within ArcMap (pre ArcGIS 10.1), the output raster was limited to a size of roughly 5,500 columns by 5,500 rows, i.e. the output raster cannot have more than about 30 million cells. Increasing the amount of computer memory has no effect on the size of the output raster when using this version.
In ArcGIS 10.1 we’ve incorporated ANUDEM 5.3 in TopoToRaster. When running on Windows XP we are now able to generate an output raster of approximately 8,500 columns by 8,500 rows. If you have a machine running Windows 7 with more than 3GB of RAM you will be able to generate an output DEM of roughly 15,000 columns by 15,000 rows.
If TopoToRaster is run in ArcGIS 10.1 for Server on a machine with Windows 7 (64 bit) with 6GB of RAM you’ll be able to produce an output DEM of approximately 25,000 columns by 25,000 rows. Using ArcGIS for Desktop – Background processing (64 bit) at 10.1 SP1 you’ll be able to produce an output raster of similar dimensions.
The volume of input data, however, can also cause TopoToRaster to run out of memory. If your input features have many millions of input vertices it is advisable to split the area up into smaller regions.
Equally, if you require a larger output raster than is currently possible, splitting the area up into manageable chunks and running TopoToRaster using the MARGIN parameter is an effective solution. The output rasters can then be combined into a single raster via a Mosaic dataset or by using the CellStatistics, Mosaic orMosaicToNewRaster geoprocessing tools.
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