Published on March 28, 2014
Urban Geography: overview Created by David Palmer Eaglecrest High School
Chapter 12 Key Issue 3 Created by David Palmer & Adapted by Megan Leach
System of cities with various levels Few cities at top level Increasing number of settlements at each lower level Larger cities provide more services than smaller towns – exists at regional, national, and global scales Urban Hierarchy Number of Business Types by Population of Colorado Cities (1899) Graph from Kuby, HGIA
Urban Geography – Urban Systems
Ranking of Census MSAs (Metropolitan Statistical Areas) of U.S., 2005 MSAs with populations over 2 million (right) 24 more MSAs have pops between 1 and 2 million 47 more between 500,000 and 1 million 74 more between 250,000 and 500,000 169 more bet. 100,000 and 250,000
Nested hexagonal market areas predicted by Central Place Theory Central Place Theory Spatial model of settlements (central places) for a nested hierarchy of market areas
Central Place Theory • Geographic assumptions (Christaller, 1930s) - featureless landscape on infinite plane - uniform population distribution • Behavioral (economic) assumptions - consumers shop at closest place possible - consumers do not go beyond the range of the good - market areas equal or exceed threshold of good • Hexagonal market areas are most efficient - non-overlapping circles leave areas unserved - higher-order central places also provide lower-order functions
Central Place Theory in action on a flat, featureless plain (e.g., Northern Germany) … and in a landscape with “locational biases” introduced by physical features What does Utah look like?
Nesting of Services and Settlements MDCs have numerous small settlements with small thresholds and ranges, and far fewer large settlements with large thresholds and ranges. Nesting pattern – overlapping hexagons
Optimal Location Within a Market Best Location in a Linear Settlement Gravity Model • Number of people vs. distance they travel for access Best Location in a Non-Linear Settlement Identify possible site Identify potential users & measure distance Divide users / distance to site Select other locations & repeat steps Select highest # (most users / location)
Rank-size Rule Rank Size Rule Nth largest city of a national system will be 1/n the size of the largest city. Example - US is close to this model - not a good model for newly urbanized countries i.e. LDC
Primate City One dominant city in a country or region. There is usually not an obvious second city Example - Paris France - 8.7 million next city Marseille - 1.2 million
Mexico Primate City Mexico is an excellent example of a Primate City model. Mexico City is dominant city in Mexico
Paris historical site and growth
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