Mapping Damascus Over Time

The city of Damascus has always intrigued me. Maybe because it is the oldest established metropolis in the Levant region and the most continuously inhabited city in the world, or maybe it is because it is in the heart of the Middle East. Regardless of the reason behind this intriguing city, it is interesting to see how the city developed over time from a spatial perspective.  I have compiled a list of eight maps to show how the city developed since the Romans occupied the Levant region of the Middle East.  The first in the series of maps shows the various empires throughout history that helped shaped the city into its early 20th century boundaries.  Then each of the following maps progressively show how Damascus grew over time.  Now with the civil war destroying this city, it would be interesting to see how the city will be altered again.

Map 1: Urban Evolution of the City of Damascus - Roman Era until the 1960's

Map 2: French cartographic map of Damascus in 1929

Map 3: Cultural Map of the city of Damascus in 1936

Map 4: Growth of Damascus from 1936 to 1994

Map 5: Population Growth of Damascus, 1981 to 2000

Map 6: The City of Damascus in 2004

Map 7: The inner-city of Damascus, Present Day

Map 8: Present day satellite imagery of Damascus

Map Sources:

Map 1: Al-Qattan, N. (2002) Litigants and Neighbours: The Communal Topography of
           Ottoman Damascus Comparative Studies in Society and History Vol 44 No 3, p 511-533.
Map 2: Bureau Topographique des Troupes Francaises du Levant (1929) Damas available from  
           http://historiccities.huji.ac.il/syria/damascus/maps/tfl_1929_damascus.html
Map 3: a) Map - Al-Qattan, N. (2002) Litigants and Neighbours: The Communal Topography of
           Ottoman Damascus Comparative Studies in Society and History Vol 44 No 3, p 511-533.
           b) Religion overlay - Khoury, P.S. (1984) Syrian Urban Politics in Transition: The Quarters of
           Damascus during the French Mandate International Journal of Middle East
           Studies Vol 16 No 4 p 507-540 (1984).
Map 4: El-Ibrahim, K. (2001) Spontaneous Settlements around the City of Damascus
           Damascus: University of Damascus, p 80Map 5: El-Ibrahim, K. (2001) Spontaneous Settlements around the City of Damascus
           Damascus: University of Damascus, p. 71
Map 6: http://en.wikipedia.org/wiki/File:Districts_of_damascus.gif
Map 7: http://www.studio-basel.com/assets/files/files/21_urban_development_of_damascus_web.pdf
Map 8: Astrium Geo. 2012. http://www.imagekind.com/Damascus-Syria--Satellite-Image-art?
           IMID=5cd73cc1-6559-42a0-a1c0-b1d745c9d45f

Applying Geographic Information Systems to Real-world Issues - Cell Tower Mapping

Case Study Proposal:

PROPSED TITLE:

The identification of 25 potential sites to construct cell phone towers around the Greater Boston Metropolitan Area

ABSTRACT 

This study will examine the potential for a design of a project in regards to a wireless phone company who is interested in expanding their communication network to receive better coverage in the Greater Boston Metropolitan area of the Commonwealth of Massachusetts.   The project will consist of sorting through many datasets available online via http://www.mass.gov/mgis/massgis.htm and then downloading key features that may assist in the creation of geodatabases.  Ultimately, this will align the various analyses of determining which datasets will reveal ideal locations for the new cell towers.  Furthermore, new developments have boosted the population in and around Boston creating a greater demand for communication technologies, especially as the networks are expanding into 4G standards.

MASS.GOV LAYERS

The following layers will establish a base foundation dataset in order to perform several different types of analyses.  Some of these datasets may be repetitive (i.e. some of the data will be downloaded for awareness) or may only be used as a reference and not in the actual analyses  (which are located immediately below).

BACKGROUND LAYERS:

Aerial photography - USGS Color Ortho Imagery (2008/2009)

Scanned Reference Maps - USGS Topographic Quadrangle Images/USGS 1:24,000 Surficial Geology Topographic Base Map Images

Vector Files - Community Boundaries (Towns) from Survey Points/County Boundaries/State Outlines/Massachusetts 2000 Urban Boundaries/Zoning/Trains/Building Footprints (2-D, from Lidar data)/Datalayers from the 2000 U.S. Census: TIGER Linework (Roads, Railroads, Rivers and Streams, Powerlines, etc.), TIGER Water Bodies, and TIGER Towns.

RASTER LAYERS

Elevation

·         Elevation (Topographic) Data (2005)

·         Digital Elevation Model (1:5,000)

·         Shaded Relief (1:5,000)

VECTOR LAYERS

Census/Statistical Data

• Datalayers from the 2010 U.S. Census
• Blocks
• Block Groups
• Tracts

Ground Suitability Data

·         Impervious Surface (raster to vector conversion)

• Land Use (2005)
• Land Use (1951-1999)
• NRCS SSURGO-Certified Soils
• Surficial Geology (1:24,000)
• Surficial Geology (1:250,000)
• Bedrock Lithology
• Priority Natural Vegetation Communities
• Prime Forest Land
• Interior Forest
• Aquifers

Conservation/Recreation (merge into one shapefile)

• Protected and Recreational Open Space/Areas of Critical Environmental Concern/Scenic Landscapes/NHESP Natural Communities/BioMap2

Infrastructure

Transportation

• MassDOT Roads
• Census 2010 TIGER Roads

Other Facilities (merge into one shapefile)

• Schools/Colleges and Universities

Physical Resources

Topography

• Elevation (Topographic) Data (2005)
• Elevation Contours (1:5,000)
• Elevation Contours (1:250,000)
• Digital Orthophoto Elevation Points
• Digital Orthophoto Topographic Breaklines
• Digital Terrain Model (DTM) Files
• Digital Elevation Model (1:5,000) from DTM Files
• Shaded Relief (1:5,000)

Water Features (Merge into one shapefile)

• Hydrography (1:25,000)/ DEP Wetlands (1:12,000) (formerly 1:5,000 Orthophoto Wetlands and Streams)/Major Ponds and Major Streams

ADDITIONAL LAYERS

Additional layers can be found at http://wireless.fcc.gov/geographic/index.htm - it is important to understand the current cell tower infrastructure in identifying gaps in coverage or where they may lay in regards to more population density in one area over another.  Also, major thoroughfares need to have consistent connectivity for travelers as well as be able to handle the influx of users on a cellular network.  This site provides maps showing FCC licensing data, regulated towers, and market area boundaries.  These maps then need to be converted into a raster file and eventually digitize to extract the generated information on the map into vector files.  Another website I would extract data from would be www.cloudmade.com.  There are no guarantees on what type of data will be available, but cloudmade.com allows you to download various shapefiles of landmarks, infrastructure, and other user-generated data that has been uploaded to openstreetmaps.com.  Acquiring traffic data will also be of value for this project in order to identify high trafficked areas of cars along major thoroughfares.

METHODOLOGY

There are several types of analyses I would use within ArcGIS in order to conduct this project.  Proximity analysis is useful in several different ways.  First and foremost, in this project the geographic constraint is 25 miles outside of the Boston city limits.  I will create an extent polygon in order to clip out each of the attributes of my shapefiles that fall outside of this area.  This will allow me to use smaller datasets and not have to be concerned with highways and schools (including colleges and universities) outside of the constraint, among many other shapefiles’ attributes.   On the other hand, some of the features are not necessarily that important by themselves.  For example, there are various types of water features or conservation/recreation layers that do not need to be standalone files.  Merging these various datasets in order to eliminate unnecessary cluster is important so there is less data to work with.  The important factor as a result is that at least the water features and conservation layers are captured since building a cell tower is not an option within these locations.  Another type of proximity analysis is buffering.  In order to identify build zones, a 1 mile buffer must be conducted around the MassDOT Roads (attribute: highway) shapefile and then the lines must be dissolved in case several highways are in close proximity to one another.   In addition, buffering must be done with a radius of 1 mile around all schools.  Once the new shapefiles are generated as a result of the buffering tool, I will overlay each of the buffer files and delete from the highway buffer, wherever the school buffer file overlaps to minimize the amount of ideal locations.

Besides proximity analysis, other types of analyses will be useful in finding ideal locations for the new cell towers.  Towers need to be located in areas where population density is higher than normal to cater toward the influx of people utilizing the network.  As a result, a population density analysis must be conducted based from census data.  Elevation data can assist in terrain analysis in order to identify any hills or peaks above and beyond 250’ above sea level.  Once this area is identified, soil and hydrographic analysis will be conducted in order to determine the ground suitability for building the tower to ensure it is placed in a strong foundation.  Lastly, since cell towers need to be in a close proximity to other cell towers, the next type of analysis I will conduct is a Line of sight analysis to ensure the newly identified locations are within a certain distance from other towers and there are no vertical obstructions and to identify the potential cell coverage. 

CONCLUSION

The results of this project should determine what areas within the Greater Boston Metropolitan region are ideal in order to build new cellular towers.  The additional datasets from the FCC website will help to alleviate any overlapping towers in order to improve the communications network.  Geospatial data of the amount of users per cell towers in and around the ideal locations would probably improve this study.  Since urban and rural areas have different needs in regards to cell tower use, towers need to be located in ideal locations, but since cell towers are known to possibly cause health issues, towers must stand clear of schools and recreational areas.

LIMITATIONS

This most anticipated roadblock will be the accuracy of all of the data.  Not all the data in the files being used has been captured in the past year.  Therefore, some of the data may be missing components crucial to a full and complete analysis of identifying ideal locations.  Further research needs to be done to confirm the validity of all the data.  For example, have any schools closed down since the shapefiles were generated or have new school been built would be questions that need to be answered.   Also, an urban legend about cell towers is often told that they cause cancer and serious health risks.  Cellular technology is a relatively newer technology and the health community is unable to confirm this suspicion as of yet; however, the public still has some reservations about them being erected nearby to residential areas.  Lastly, the wireless company may have to pay rent for the location of the tower especially if it’s close to residential areas because of the depreciation of property that it will affect the neighborhoods the towers are being built around.