Cities Growth Page - Revision history

LaScalad: /* Meetings */

2014-06-30T03:11:27Z

Meetings

← Older revision		Revision as of 03:11, 30 June 2014
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	= Meetings =		= Meetings =

	'''~~Saturday 28~~''' : 6:00 pm ~~Morgan, Rohan, Bernardo and Diana meet to resolve paper and begin presentation~~ (coffee shop) <br>		'''Tuesday 1''' : 6:00 pm EVERYONE meets (coffee shop) <br>
	'''~~Sunday 29~~''' : 6:30 pm Everyone meets (~~outside~~ lecture hall)		'''Wednesday 2''' : 10:45 am - 12 pm Everyone meets (lecture hall) <br>

	----		----
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	done :		done :

			'''Saturday 28''' : 6:00 pm Morgan, Rohan, Bernardo and Diana meet to resolve paper and begin presentation (coffee shop) <br>
			'''Sunday 29''' : 6:30 pm Everyone meets (outside lecture hall)
	'''Monday 23''' : 1:00 pm Morgan, Bernardo, Michael and Claire meet to refine cities lit search at 1pm (coffee shop) <br>		'''Monday 23''' : 1:00 pm Morgan, Bernardo, Michael and Claire meet to refine cities lit search at 1pm (coffee shop) <br>
	'''Tuesday 24''' : 4:15-5:00 pm James, Alex and Diana meet to discuss data processing goals (lecture hall) <br>		'''Tuesday 24''' : 4:15-5:00 pm James, Alex and Diana meet to discuss data processing goals (lecture hall) <br>

LaScalad: /* Ongoing work */

2014-06-27T16:20:58Z

Ongoing work

← Older revision		Revision as of 16:20, 27 June 2014
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	'''Model Development'''		'''Model Development'''

	* ~~Work on~~ NetLogo Model (Ernest)		* Write documentation for and debug NetLogo Model (Ernest)
			- Model output in this format: [Time step, Point 1 connected at that time step, Point 2 connected] <br>

	* Work on Python Model (Alberto, Claire) <br>		* Work on Python Model (Alberto, Claire) <br>
	- ~~Take differential elevation into account~~ <br>		- Implement pseudocode <br>
	- ~~Add resources (or benefits) into the model <br>~~		- Model output in this format: [Time step, Point 1 connected at that time step, Point 2 connected] <br>
	~~- Remove pixels directly around edges from probability calculations <br>~~
	~~- Program~~ in ~~some (adjustable?) ability to make loops (if~~ time) <br>

	Ultimate goals for Python model: <br>		Ultimate goals for Python model: <br>
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	'''Data Analysis'''		'''Data Analysis'''

	* Analysis of Networks (Alex, James, Diana)		* Analysis of Networks (Alex, James, Diana) <br>
			- Use NetworkX to generate shape files for Claire <br>
			- Use NetworkX to calculate network metrics from models <br>

	* QGIS (Claire, James)		* QGIS (Claire, James) <br>
			- Network visualization <br>
			- Calculation of network metrics for Avignon, Paris, and empirical ant networks <br>

	'''Refining Project Goals'''		'''Refining Project Goals'''

	* Discuss ~~specific kinds of city networks that will provide good comparisons to ant foraging trails from one nest and between many nests~~ (Morgan, Bernardo, ~~Michael~~, ~~Claire~~)		* Discuss paper objectives (Morgan, Bernardo, Diana, Rohan) <br>
			- Work through remaining conceptual issues
			- Decide on paper content
			- Create skeleton presentation

	'''Continued Literature Review'''		'''Continued Literature Review'''

LaScalad: /* Meetings */

2014-06-27T16:16:18Z

Meetings

← Older revision		Revision as of 16:16, 27 June 2014
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	= Meetings =		= Meetings =

	'''~~Monday 23~~''' : 1:00 pm Morgan, Bernardo~~, Michael~~ and ~~Claire~~ meet to ~~refine cities lit search at 1pm~~ (coffee shop) <br>		'''Saturday 28''' : 6:00 pm Morgan, Rohan, Bernardo and Diana meet to resolve paper and begin presentation (coffee shop) <br>
	'''~~Tuesday 24''' : 4:15-5:00 pm James, Alex and Diana meet to discuss data processing goals (lecture hall) <br>~~		'''Sunday 29''' : 6:30 pm Everyone meets (outside lecture hall)
	~~'''Thursday 26~~''' : 1:00 pm ~~EVERYONE~~ (lecture hall) ~~<br>~~

	----		----
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	done :		done :

			'''Monday 23''' : 1:00 pm Morgan, Bernardo, Michael and Claire meet to refine cities lit search at 1pm (coffee shop) <br>
			'''Tuesday 24''' : 4:15-5:00 pm James, Alex and Diana meet to discuss data processing goals (lecture hall) <br>
			'''Thursday 26''' : 1:00 pm EVERYONE (lecture hall) <br>
	'''Sunday 22''' : 6:00 pm (Arrive at 5:30 pm to discuss Python model)		'''Sunday 22''' : 6:00 pm (Arrive at 5:30 pm to discuss Python model)
	'''''Thursday 12''' : 6:00 pm - 7:00 pm <br>''		'''''Thursday 12''' : 6:00 pm - 7:00 pm <br>''

LaScalad: /* Analysis Goals */

2014-06-24T19:39:07Z

Analysis Goals

← Older revision		Revision as of 19:39, 24 June 2014
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	'''Network Analyses of Cities: Relevant Measures'''		'''Network Analyses of Cities: Relevant Measures'''
	1. Edge length distribution (Courtat "Hypergraphs...")
	2. Degree distribution		1. Edge length distribution (Courtat "Hypergraphs...") <br>
	3. Route factor (Gastner and Newman 2006B)		2. Degree distribution <br>
	4. Total length (Gastner and Newman 2006B)		3. Route factor (Gastner and Newman 2006B) <br>
	5. Measures of centrality: betweenness, closeness (Courtat "Centrality Maps...")		4. Total length (Gastner and Newman 2006B) <br>
	6. Measures of robustness		5. Measures of centrality: betweenness, closeness (Courtat "Centrality Maps...") <br>
			6. Measures of robustness <br>
	7. Claire has some of her own indices we might like to use...		7. Claire has some of her own indices we might like to use...

LaScalad: /* Analysis Goals */

2014-06-24T19:38:15Z

Analysis Goals

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 = Analysis Goals =
 Scale-free foraging patterns are currently a hot topic in animal movement biology.  Typically, these scale-free patterns are identified through time series data - how far does the animal move before it changes direction?  Are these step lengths drawn from a power law distribution?
@@ Line 133: / Line 139: @@
 . Measures of robustness (see Cook 2014) <br>
 . For ant travel networks, robustness is between 14 and 72%. <br>
 - Diana

LaScalad: /* Analysis Goals */

2014-06-23T15:02:05Z

Analysis Goals

← Older revision		Revision as of 15:02, 23 June 2014
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	9. Measures of robustness (see Cook 2014) <br>		9. Measures of robustness (see Cook 2014) <br>
	10. For ant travel networks, robustness is between 14 and 72%. <br>		10. For ant travel networks, robustness is between 14 and 72%. <br>

			- Diana

	= Exploratory Notes =		= Exploratory Notes =

LaScalad: /* Alfred's model */

2014-06-23T15:01:41Z

Alfred's model

← Older revision		Revision as of 15:01, 23 June 2014
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	Unfortunately, the work done in which the self-assembling dendritic trees seek out their electromagnetic resources in such a way so as to demonstrate Levy-Walks was never published. It would be interesting to know if there was any unpublished theoretical work done for these resource searching trees in the same flavor as for the self-assembling wires.		Unfortunately, the work done in which the self-assembling dendritic trees seek out their electromagnetic resources in such a way so as to demonstrate Levy-Walks was never published. It would be interesting to know if there was any unpublished theoretical work done for these resource searching trees in the same flavor as for the self-assembling wires.

	Alex		- Alex

	== Slime mold ==		== Slime mold ==

LaScalad: /* Other thoughts on modeling... */

2014-06-23T15:01:02Z

Other thoughts on modeling...

← Older revision		Revision as of 15:01, 23 June 2014
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	Here's my idea for a network model. We start out with a grid (fine, but still a grid; a continuous map may be too computationally intensive), with each cell possessing some attributes: elevation, proximity to resources, and proximity to roads. We choose a root at random (or not) and add nodes by the following process: within the field of view (an NxN square surrounding each node), choose the cell whose edge has minimum cost. The cost is determined by taking into account the following values: the effective length of the proposed edge (factoring in topography), the length of the path from the root to the proposed node, (from the Gastner and Newman paper), the proximity of the new node to the nearest resource, and the proximity of the node to existing roads (the closer it is to existing roads, the less we need to make it a node in the road network). The resource proximity part should be a function of population density: the greater the population/the smaller the resource budget of the population, the more heavily weighted this part. If the resource budget is sufficient (this will only be temporary), no node is added; we can ignore these steps since nothing happens. The foraging model will be the exact same model except roads are not built on the network; the network still exists as a set of repeated movement patterns, but there are no roads, and so the proximity-to-roads part of the cost function has weight 0. Foraging animals probably do not use their much-traveled paths to go anywhere other than the end of the path. If the resource budget situation is dire and there are no really useful cells in the field of view, there is an option to jump to a random location that is nearby but outside the field of view and add the resulting patch to the field of view. Of course, in most cases such a jump would result in fairly long proposed edges, which would be costly, but hopefully the benefit of landing near a resource outweighs the cost. The jumps can have a distance from the field of view drawn from a heavy-tailed distribution.		Here's my idea for a network model. We start out with a grid (fine, but still a grid; a continuous map may be too computationally intensive), with each cell possessing some attributes: elevation, proximity to resources, and proximity to roads. We choose a root at random (or not) and add nodes by the following process: within the field of view (an NxN square surrounding each node), choose the cell whose edge has minimum cost. The cost is determined by taking into account the following values: the effective length of the proposed edge (factoring in topography), the length of the path from the root to the proposed node, (from the Gastner and Newman paper), the proximity of the new node to the nearest resource, and the proximity of the node to existing roads (the closer it is to existing roads, the less we need to make it a node in the road network). The resource proximity part should be a function of population density: the greater the population/the smaller the resource budget of the population, the more heavily weighted this part. If the resource budget is sufficient (this will only be temporary), no node is added; we can ignore these steps since nothing happens. The foraging model will be the exact same model except roads are not built on the network; the network still exists as a set of repeated movement patterns, but there are no roads, and so the proximity-to-roads part of the cost function has weight 0. Foraging animals probably do not use their much-traveled paths to go anywhere other than the end of the path. If the resource budget situation is dire and there are no really useful cells in the field of view, there is an option to jump to a random location that is nearby but outside the field of view and add the resulting patch to the field of view. Of course, in most cases such a jump would result in fairly long proposed edges, which would be costly, but hopefully the benefit of landing near a resource outweighs the cost. The jumps can have a distance from the field of view drawn from a heavy-tailed distribution.

	Rohan		- Rohan

	From "Foraging Theory", Stephens and Krebs, Princeton University Press, 1986		From "Foraging Theory", Stephens and Krebs, Princeton University Press, 1986
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	"Formal models are valuable because they permit both rigorous analysis and testing. Optimization models consist of three components - decision assumptions, currency assumptions, and constraint assumptions. The decisions studied by conventional foraging models relate to prey choice and patch exploitation; the currency in these models is long-term average-rate maximization (maximum energy input per unit time); and the constraints are exclusivity of search and exploitation (you cannot search for food and consume food at the same time); sequential, random search; and the assumption of complete information (the forager behaves as if it knows the rules of the model)." - pages 11 and 12, parenthetical explanations mine		"Formal models are valuable because they permit both rigorous analysis and testing. Optimization models consist of three components - decision assumptions, currency assumptions, and constraint assumptions. The decisions studied by conventional foraging models relate to prey choice and patch exploitation; the currency in these models is long-term average-rate maximization (maximum energy input per unit time); and the constraints are exclusivity of search and exploitation (you cannot search for food and consume food at the same time); sequential, random search; and the assumption of complete information (the forager behaves as if it knows the rules of the model)." - pages 11 and 12, parenthetical explanations mine

	Diana		- Diana

	== Concept Map/Table ==		== Concept Map/Table ==

LaScalad: /* Analysis Goals */

2014-06-23T15:00:18Z

Analysis Goals

← Older revision		Revision as of 15:00, 23 June 2014
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	In addition, it would be interesting to note how properties unique to foragers but not to cities (like having to return to a home or nest, or not creating permanent roads) change the shape, structure and fundamental properties of a network. As Aaron Clauset pointed out, simply being scale-free is not that interesting in and of itself. What other properties of networks might be interesting and/or informative? (Clustering... others?)		In addition, it would be interesting to note how properties unique to foragers but not to cities (like having to return to a home or nest, or not creating permanent roads) change the shape, structure and fundamental properties of a network. As Aaron Clauset pointed out, simply being scale-free is not that interesting in and of itself. What other properties of networks might be interesting and/or informative? (Clustering... others?)

			'''Network Analyses of Ant Foraging Trails: Relevant Measures'''

			1. Total area foraged by the colony increases with population^(2/3) (Jun 2003) <br>
			2. The metabolic cost of foraging increases linearly with population (Jun 2003) <br>
			3. Ant networks represent a balance between total length and route factor (Buhl 2009) <br>
			4. Foraging networks are typically dendritic in form (Buhl 2009) <br>
			5. Route factor = 1.13 (Buhl 2009) <br>
			6. Trail branching angle = 49.33 + or – 29.9 degrees (Buhl 2009) <br>
			7. Are node distributions different from complete spatial randomness? (see Cook 2014) <br>
			8. Measures of centrality: betweenness, closeness (see Cook 2014) <br>
			9. Measures of robustness (see Cook 2014) <br>
			10. For ant travel networks, robustness is between 14 and 72%. <br>

	= Exploratory Notes =		= Exploratory Notes =

LaScalad at 14:58, 23 June 2014

2014-06-23T14:58:25Z

← Older revision		Revision as of 14:58, 23 June 2014
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	'''ALSO SEE PAPERS IN DROPBOX'''		'''ALSO SEE PAPERS IN DROPBOX'''

	== Analysis Goals ==		= Analysis Goals =

	Scale-free foraging patterns are currently a hot topic in animal movement biology. Typically, these scale-free patterns are identified through time series data - how far does the animal move before it changes direction? Are these step lengths drawn from a power law distribution?		Scale-free foraging patterns are currently a hot topic in animal movement biology. Typically, these scale-free patterns are identified through time series data - how far does the animal move before it changes direction? Are these step lengths drawn from a power law distribution?
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	In addition, it would be interesting to note how properties unique to foragers but not to cities (like having to return to a home or nest, or not creating permanent roads) change the shape, structure and fundamental properties of a network. As Aaron Clauset pointed out, simply being scale-free is not that interesting in and of itself. What other properties of networks might be interesting and/or informative? (Clustering... others?)		In addition, it would be interesting to note how properties unique to foragers but not to cities (like having to return to a home or nest, or not creating permanent roads) change the shape, structure and fundamental properties of a network. As Aaron Clauset pointed out, simply being scale-free is not that interesting in and of itself. What other properties of networks might be interesting and/or informative? (Clustering... others?)

	== Exploratory Notes ==		= Exploratory Notes =

	== Alfred's model ==		== Alfred's model ==
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	Vultures <br>		Vultures <br>

	= Data =		= Modeling and Data Notes =

			== Data ==

	OpenStreet Maps are available to import directly into QGIS. Go to Vector -> OpenStreetMaps <br>		OpenStreet Maps are available to import directly into QGIS. Go to Vector -> OpenStreetMaps <br>
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	Diana		Diana

	= Concept Map/Table =		== Concept Map/Table ==

	Ants		Ants
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	*Reasonably likely to move location of house		*Reasonably likely to move location of house

	= Santa Fe Historical Maps =		== Santa Fe Historical Maps ==

	Potential Sources To Be Sought Out For Maps		Potential Sources To Be Sought Out For Maps