Final Lab: To Dig or not to Dig?

Introduction:

Beginning with its roots in antiquarianism, archaeology has progressed throughout the past two centuries, shifting from a discipline concerned primarily with physical remains to a discipline that aims to reconstruct past societies and human behaviors.  Archeologists seek to reconstruct past behavior through the examination of physical remains, whether in the form of artifacts, features, sites, or regions.  The study of archaeology has taken root throughout the majority of the world and there are still many places left to be uncovered.  GIS is fast becoming an important tool for helping to reconstruct past societies.  Much of the GIS work that is done is based on specific sites and regions, however archaeology can benefit from GIS on a global scale.  My first goal in the implementation of this project was to incorporate GIS with worldwide archaeological information to gain understanding of the time depth within the study of archaeology.  Geographically examining where archaeological work has been done in the past aids in proposing where archaeological work should be done in the future.  Additionally, archaeologists need to understand the specific environment in which they are working in order to understand what sort of features will preserve and which will decay.  My second goal was to find the environments that are most suitable for preserving archaeological materials and display them geographically so that archaeologists can then interpret where to look and was to avoid.  While both of these aims are helpful, they are still not sufficient to understanding where to dig.  My ultimate purpose in the following project was to combine the information I gathered about the locations of archaeological material and density of dwelling throughout time with the suitability of the environment to preserve the archaeological materials in order to discover the most suitable places to dig and study with the most success is the ultimate purpose of the following project.

Methods:

To begin this project, I needed to find the correct data.  Since the field of archaeology is only just beginning incorporate GIS technologies into their study of past remains, there is very little data available.  However, since my goal was to discover where archaeological sites are, the data was not too hard to collect.  I found coordinates provided by Wikipedia about the location of archaeological sites throughout the world, which I then created an excel file for and imported that into GIS to project.  The location of current archaeological sites is representative of where past societies were dwelling in addition to where current societies dwell.  The other data that I used were about the environmental regions of the world, provided by ESRI. 

The next step in the process was to covert the features provided in the environmental shapefile into raster, in order to perform suitability analysis upon them.   Once it was in a raster form, I was able to reclassify the environments based on their suitability in preserving archaeological materials.  I obtained this information from the text Archaeology: Theories, Methods, and Practice by Paul Bahn and Colin Renfrew (2008) .  According to this text, the best places for archeological preservation are extremely cold environments, frozen environments, marsh environments, wetlands, bogs, and extremely dry, arid, and desert environments.  The worst places for archaeological preservation are temperate climates, like most tropical regions.  Through these guidelines, I was able to reclassify the environmental layer in order to show the most suitable environments for archaeological preservation.

Following this step, I did a spatial join archaeological sites point layer with a shape file of the world containing country information.  This enabled me to spatially display the count and the density of dwelling that is known and recorded in the archaeological record.  This too had to be converted into a raster in order to perform the next step, calculation.

Raster calculation takes the number that is assigned to each cell on a given map and uses a mathematical function (in this case, I added) to combine with the cell that spatially corresponds to on a different raster map.  The raster calculation of the environmental suitability layer and the density of dwelling layer were next.  I implored the spatial analyst toolbar and weighted the density of the dwelling layer slightly higher, in order to counteract the extremely high preservation quality of extremely uninhabitable areas, like Antarctica.   I reclassified the results in order to make the data more easily understandable and divided it into 5 categories: least suitable, unsuitable, somewhat suitable, suitable and most suitable.

I then thought it would be important to display which continents had the most suitable places to dig and discover more archaeological information about so I created a graph based on the above data that I had gathered and spatially displayed.  I used the graph maker in the tool bar and displayed my results of the raster calculation for the variety of sites throughout the world.

Additionally, it is important to consider how the layout of the maps and their constituents will be displayed on the final output.  For a frame a reference, I inserted a globe with the locations of archaeological sites throughout the world.  I also incorporated the theme of archaeology into the color scheme, using earth tones to tie the elements together.  Also, I aimed for my display to be hierarchically based through the size of each of the features and also arranged in a way that is aesthetically pleasing as well as revealing of the relative importance of each element.

Results:

The methods that I implored were highly valuable and helpful in addressing the main purpose of this project, that being the locations in which archaeologists should be digging.  Interestingly, many of the places that had the best preservation of archaeological material were places that were completely inhabitable.  The relationship between good preservation and density of dwelling seemed to be inversely related. However, there did seem to be a good amount of evidence suggesting where some very good, suitable places were to study and gather information about the archaeological record.  For example, some highly suitable places to dig as revealed by this study are in Mexico, parts of Africa, and in the American southwest to name a few.  Some unsuitable places are the northern parts of Canada, and Russia as well as the tropical regions of northern South America. 

Another result of this assessment of where archaeologists should be digging was the visual display of the wealth of information about the archaeological record according to the above methods that is offered by continent.  This graph reveals the continents that have the most archaeological materials as well as the best environment suitable for preservation.  According to this graph, the best continent to conduct archaeological research is Asia.  Interestingly, this is where much of the archaeological research has been done that has occurred in the past decade.  It is important to keep in mind however, that Asia is also the largest continent and therefore its sheer size and prevalence of past material could also skew some of the results.

Conclusion:

I used GIS and many of its important functions and features in the quest to find the best preserved and most prevalent amount of information in the archaeological record.  The importance of understanding this information in a geographic context is extremely important as it does not just display where the best site are, but it is also able to be spatially referenced.  Archaeologists can look at this map in GIS and locate the exact coordinate of past sites and find the exact location of further archaeological projects that have not yet been undertaken.  I created these maps in a hope that they could be used as a tool for archaeologists in the future but also as a way to show the importance of GIS in archaeology.  Many archaeologists are worried about the limitations of GIS and how it can skew data and create misunderstandings about the archaeological record and GIS has not been as actively incorporated into the study as it has been in many other social sciences.  There are many further applications of GIS with archaeology, most having to do with reconstructing past societies and also making predictions, through the predicative model feature in ArcGIS about how exactly humans in the past shaped their environment.  The possibilities of GIS within archaeology are endless; the field of archaeology just needs some GIS experts to help them move forward into this impressive technology.

Lab Seven: Week 9

        Interpolation is a very important function with ArcGis.  It enables to prediction of values throughout a spatial area without having all of the values for that area.  It takes points and estimates based on various mathematical and spatial functions what the values of the surrounding point will be.  For example, rainfall in Los Angeles county is recorded by 62 different precipitation stations spread throughout the county.  ArcGis is able to predict what the rest of rainfall values are by using the various interpolation methods of kriging, Inverse distance weighting, or splining.

In this example, Los Angeles county recent rainfall seems to be higher than the normal county rainfall of Los Angeles county reported over the last few years.  However, it is difficult to determine the true difference between recent and normal rainfall based on the maps because each interpolation method gives different highs and lows.  Additionally, it could be the case that some places have received more rainfall and some places have received less rainfall on average this season than the normal season rainfall and it is difficult to determine accurately where these places are with the interpolation method although a change can be calculated by subtracting the season total from the season normal.  Accuracy needs to be called into question when assessing, however it is important to keep in mind that the interpolation is the only way to produce information for the whole county. 

The methods that I employed to create the three rainfall maps of Los Angeles County were kriging, splining, and inverse distance weighting.  The method that I think represented the data the best is the splining method.  Splining "estimates values using a mathematical function that minimizes overall surface curvature.  This results in a smooth surface that passes exactly through the input points while minimizing the total curvature of the surface.  It can predict ridges and valleys in the data" and because of this it is best for representing the smoothly varying surfaces, like temperature (ArcUser, esri.com 2004: 35).  

Lab Six: 2009 Station Fire: Fire Hazard Analysis

Tutorial: Slope Analysis

Tutorial: Fuel/surrounding Areas

2009 Los Angeles Station Fire: Fire Hazard Analysis

            The Los Angeles Station Fire of 2009 located primarily in the Angeles National Forest had a drastic effect on the surrounding environment, people and government.  According to the US Forest service, “the Station Fire burned 160,577 acres, destroyed 89 structures and killed two LA County firefighters.”  The fire, officially reported on August 26, 2009 burned and was not fully extinguished until October 16, 2009.  By studying the elevation, slope and vegetation values located within the surrounding areas of the station fire, I provide an analysis of places where fire hazards are highest, finding that much of the most hazardous locations are within the station fire’s parameter.

            I initially began the analysis by obtaining a digital elevation model of the area, vegetation information, and the extent of the fire.  By combining slope information, provided by the elevation, with the National Fire Protection Association’s (NFPA) standards of slope hazard, I created a map incorporating the station fire that shows which points are at the highest risk of fire according to their slope.  Next, I reclassified the vegetation/fuel information in a similar manner according to the NFPA’s standards on which fuels are at risk of perpetuating fire.  Through these two maps, it became evident where fire risks are most likely to occur.  However, most significant to the analysis of the fire hazards near the 2009 Station Fire, was combing both sets of data, by using the Raster calculator,  to create a final analysis showing where slope and fuel risks were highest.  The final analysis shows that some of the highest risk areas were within the confines of the station fire.  It also shows that for the most part, the type of fuel and the slope of the elevation are closely related.

            This type of spatial analysis, although frustrating at times, is very important in providing information as to where fire risks are most likely to occur and therefore where the most extensive preventative measures should be taken.  My personal issues with creating came with not having a solid knowledge of what exactly I was attempting to create at the beginning of the process.  However, as I began to work through it, I began to understand what the activity entailed.  I still wish I had more time to be instructed in these methods as I find them complicated and still do not fully understand the details.  But as it were, it is still yields quite an accomplished feeling when the output of your map reveals and addresses a spatial problem that can now be helped in order to lead to a solution.

References: 

http://gis.ats.ucla.edu/

http://egis3.lacounty.gov/eGIS/

http://frap.cdf.ca.gov/ 

Lab 5: Landfill Suitability Analysis Factors

In order to implement the large undertaking of establishing a landfill, it is necessary to consider many different factors.  This map shows the land suitability factors for a proposed landfill in Gallatin County in Montana.  Taking into consideration such factors as slope of elevation, distance to landfills, soil drainage, streams, and land cover, helps to address risk factors that come along with the establishment of landfills, as demonstrated by the LA times article addressing the health issues associated with the landfill in Kettleman City, California.

            GIS is an extremely useful tool that should be used to address such issues as health concerns.  If Kettleman City had applied the analysis shown here for Gallatin County, it could have avoided many of the issues that they have since faced.  GIS has the unique capability to combine a number of important factors in deciding where to place a landfill in order to produce a final report.    

By studying slope elevation, the ability for waste to flow and be transferred is addressed, and therefore promotes a landfill at an elevation with little slope.  An analysis of the location of current landfills in the county will also aid in determining where to place a new landfill.  By examining environmental factors, such as soil drainage and streams, the risk of contamination and in turn health risks is decreased.  And by combining all these factors with the type of land cover, the GIS is able to provide an analysis of where it seems most suitable to have a landfill.

Kettleman City’s main concern is the risk that the landfill in their area has upon their health.  While the above factors address this concern indirectly, GIS can be implemented to further address these concerns.  In an attempt to prove to the landfill companies that the landfills were indeed the main contributing factor to birth defects in the city, a GIS map can be created mapping the communities that reported birth defects.  Additionally, the commissioner of the map of Gallatin County could include other factors, such as distance from schools and other places in which children congregate in order to further prevent instances of health problems.

The possibilities for GIS and spatial analysis to prevent health risks are almost endless.  The more the government or private companies use GIS to help solve and prevent risks, the more the risks will decrease.  In such places as Gallatin County, the landfill is sure to be placed in a location that will have the least amount of risk because of its implementation of GIS, and in such places as Kettlemen City hopefully GIS can help prevent further issues with landfill and health risks in the future. 

                Although the mandated rule that marijuana dispensaries in the city of Los Angeles must not be located within 1000 feet of places in which children congregate has good intentions, it is not practical.  When examining the geographic elements of the question, it becomes clear that very few schools are in fact located within 1000 feet of the majority of marijuana dispensaries in the city.  A law, therefore, preventing the establishment of medical marijuana buildings would waste funds shutting down on a few marijuana locations.

 Additionally, it should be noted that the majority of these marijuana dispensaries do not promote themselves and do not intend to draw in children.  Many of these places are inconspicuous and unlike the tobacco industry, do  not rely on advertisements in order to gain revenue.  These places are in no way targeting youth, as shown geographically in that they do not intend to locate their establishments close to schools, and therefore, it should not be an issue as to whether or not they are within 1000 feet.  The issue of marijuana exposure to children is not a matter of the location of these dispensaries, more a matter of the prevalence of marijuana and if the city really wished to prevent the exposure, they would outlaw the dispensaries all together.  Making a small, useless legislation will not change whether or not children are exposed and can get a hold of marijuana, it will, once again, only waste the city’s money that should be going to schools anyways.
Also, elements of this law do not make sense and prove that it is not a well-thought out law and would again, wast the governments money.  It claims it aims to end the late-night life promoted by the dispensaries and make them shut down at 8pm, but this does not have anything to do with schools, since schools are not in session at this time anyways.  This therefore would increase traffic during school hours, actually going against what the law intended.

If, however, the Marijuana dispensaries continue to expand into areas that are increasingly nearby places in which children congregate, governmental action should be taken.  Since however, it is clear that they are not geographically nearby it would simply be a waste of money to implement such a law. 

References:

UCLA GIS Mapshare

Census.gov- TigerLines

maps.google.com

Lab 4- Digitizing Iraq Map

Brynn Kurtz: Lab 3: Geocoding

In recent news, gas stations have become the topic of many discussions.   There is constant dialogue regarding the increasing gas prices, as well as frequent mention about gas station robbery and fraud threats at gas stations.  And in conjunction with the growing "green" movement, there have been many concerns raised about the negative environmental impact and the effect that the fumes might have on human health.  In this geocoding lab, I chose to look at the relationship between gas stations and schools hoping to address  these questions and show that there is potentially a real danger by placing gas stations so close to schools.

The geocoded addresses presented in the map are gas stations in Contra Costa County, a diverse county in the San Francisco Bay Area.  In the map, I chose to highlight two different communities; Richmond, which  is generally poorer than the rest of the county, and Danville, which is where more affluent residents reside. I was hoping that there would be a discrepancy in the gas station relations to school, however there did not seem to be one.  However, this does not necessarily null my hypothesis that gas stations can have a negative impact on students.  My map simply presents the information, but a further project would actually be to collect data based on crime and health in young students in both of these areas.  Nevertheless, the process of geocoding aided was vital in gaining accurate distances from schools.  By using buffers around the geocoded points, I was able to determine which schools might have more risk factors than others.

Geocoding is not only beneficial for gaining address information but it also reveals significant information about the area that is being geocoded.   For example, in order to begin the process of geocoding, I needed to obtain road information about the area in question, Contra Costa County.  Once my points were then geocoded on the map, it was interesting to examine the trend of geocoded points, the gas stations.  I found that gas stations, as well as schools (which I obtained through a point shapefile of California) tended to be along the main roads.  Very rarely was there an outlier.  This information was a byproduct of my original question but can be very helpful in addressing further questions.  This proves that geocoding can be extremely beneficial in many ways that are not initially expected.

Although the geocoding process did not fully answer my question of whether or not gas stations have a negative effect on schools and students, it was still beneficial in understanding other trends in Contra Costa County and can aid in further research on this question.

References:
Contra Costa County road info: http://www2.census.gov/cgi-bin/shapefiles2009/national-files (contained address information in the attribute table)
Contra Costa Shapefile: http://gis.ats.ucla.edu/
Contra Costa School information:  http://gis.ats.ucla.edu/
Gas Station Address Information: http://www.automobilemag.com/31/california/contra_costa/gas_prices.html

Brynn Kurtz: Lab 2 Potential Wastewater Plant Sites

Brynn Kurtz: Week One Lab: Proposed Airport Expansion