updated readme

Author: Andrew Crooks

CSS600Models/README.md

@@ -14,7 +14,7 @@ Click on the links to see a selection of course projects from [Fall 2014](https:
 [Sample Syllabus](https://www.dropbox.com/s/xmi2qgeosmxgdw5/CSS600.pdf?dl=0)
 
 ## Release Notes
-Please note these models ***come as is***. Furthermore, no personal information is given on who created these models (unless there is a publication withit, but I need to acknowledge all the students who submitted the models, you know who you are! This project would not be possible without you!), What explanations there are given by the README file for each model (these mainly come in the form of abstracts from the papers that were submitted with the models). **No further explanations, support etc. will be given**. Lastly, these models are only given for pedagogical reasons and where possible each model has been upgraded to NetLogo 6.1.
+Please note these models ***come as is***. Furthermore, no personal information is given on who created these models (but I need to acknowledge all the students who submitted the models, you know who you are! This project would not be possible without you!), What explanations there are given by the README file for each model (these mainly come in the form of abstracts from the papers that were submitted with the models). **No further explanations, support etc. will be given**. Lastly, these models are only given for pedagogical reasons and where possible each model has been upgraded to NetLogo 6.1.
 
 For those interested, models with "***(GIS)***" next to them are ones which utilize GIS data.
 

CSS645Models/GentrificationDC/DC_Gentrification.nlogo

@@ -1,10 +1,3 @@
-;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
-;;  Using Agent-Based Modeling to Show Gentrification in a Southeast area of the District of Columbia
-;;
-;; This program is
-;; written in NetLogo 5.0.3 utilizing the GIS extensions
-;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
-
 
 ; Load NetLogo extension to handle GIS functionality
 extensions [gis]

CSS645Models/Grocery_Store/README.md

@@ -5,7 +5,7 @@
 "A model of a population of agent-based simulated shoppers within a stylized instore shopping environment presents agents with impulse items for purchase as well as educational and entertainment distractions. Agents are allowed to make purchases according to their individual propensity to make impulse purchases as a function of walking speed, reflection of other agents’ shopping behavior and interest in novelty. Initial model results show that groups of shoppers walking through the store produce density waves of impulse purchases resulting from ‘shopper jams’ analogous to traffic jams along roadways in times of moderate density traffic. These shopper jams of higher density waves of shoppers may be the result of interactions among like-minded shoppers who find themselves in close proximity examining items of mutual interest. The model illustrates how cascades of impulse purchases may occur when retailers design their stores to allow rapid production and persistence of shopper jams so that shoppers mimic actions of other shoppers with similar interests who remain in close proximity."
 
 ##  
-Graphic of flow diagram of model, left hand shows initial configuration phase of the model that creates the dynamic environment. Agents that have children have a greater interest in entertainment versus those without children who have and interest in education, such as cooking tips. The right hand of the diagram shows the faster variation in interest in purchasing behavior.
+"Graphic of flow diagram of model, left hand shows initial configuration phase of the model that creates the dynamic environment. Agents that have children have a greater interest in entertainment versus those without children who have and interest in education, such as cooking tips. The right hand of the diagram shows the faster variation in interest in purchasing behavior."
 ![flow diagram](FlowDiagram.png)
 
 

CSS645Models/Hurricane_Evacuation_Model /NC_Model.nlogo

@@ -223,7 +223,6 @@ to update-globals  ;; designates areas that are safe and not-safe
 
 
 end
-
 @#$#@#$#@
 GRAPHICS-WINDOW
 148
@@ -552,8 +551,6 @@ Adding a simulate hurricane to specifically designate evacuees.
 Limit number of evacuees to a "safe zone" city. 
 Block roads with flooding. 
 Determine return to home city timeframe and have agents return if safe.
-
-## CREDITS AND REFERENCES
 @#$#@#$#@
 default
 true

CSS645Models/Hurricane_Evacuation_Model /README.md

@@ -2,7 +2,7 @@
 
 ## Abstract
 
-Evacuation of coastal cities and towns need to happen in short notice when a hurricane or other natural hazard is about to occur. Evacuation modeling has been conducted for several years, but not much has occurred for evacuations along the mid-Atlantic coast. This model uses geospatial information systems in concert with agent-based modeling to show the evacuation of coastal cities to safe zones inland and helps to determine where potential choke points could occur in the event of a wide scale evacuation. The model reveals the most likely inland destinations where evacuees would potentially travel. The paper also discusses areas of improvement and future research to be incorporated into this work. The results of this model can be enhanced to help with decisions on when and where to travel in the unfortunate case of a large-scale natural hazard emergency.
+"Evacuation of coastal cities and towns need to happen in short notice when a hurricane or other natural hazard is about to occur. Evacuation modeling has been conducted for several years, but not much has occurred for evacuations along the mid-Atlantic coast. This model uses geospatial information systems in concert with agent-based modeling to show the evacuation of coastal cities to safe zones inland and helps to determine where potential choke points could occur in the event of a wide scale evacuation. The model reveals the most likely inland destinations where evacuees would potentially travel. The paper also discusses areas of improvement and future research to be incorporated into this work. The results of this model can be enhanced to help with decisions on when and where to travel in the unfortunate case of a large-scale natural hazard emergency."
 
 ##  
 

CSS645Models/Information_Propagation /Information_Propagation.nlogo

@@ -1,4 +1,3 @@
-
 ;;Simulate information propragation in physical and cyber spaces
 ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
 
@@ -68,45 +67,45 @@ end
 to setup-USstates
   file-open "Data/stateid.txt" let patch-sid file-read file-close
   (foreach sort patches patch-sid
-     [ ask ?1 [ set layer-sid ?2 ] ] )  ;store stateid in layer-sid
+     [ [?1 ?2] -> ask ?1 [ set layer-sid ?2 ] ] )  ;store stateid in layer-sid
   ask patches
     [set pcolor layer-sid]
 
   file-open "Data/statedensity.txt" let patch-sdens file-read file-close
   (foreach sort patches patch-sdens
-     [ ask ?1 [ set layer-sdens ?2 ] ] ) ;store statedens in layer-sdens
+     [ [?1 ?2] -> ask ?1 [ set layer-sdens ?2 ] ] ) ;store statedens in layer-sdens
 
   file-open "Data/govid.txt" let patch-gid file-read file-close
   (foreach sort patches patch-gid
-     [ ask ?1 [ set layer-govid ?2 ] ] )
+     [ [?1 ?2] -> ask ?1 [ set layer-govid ?2 ] ] )
 
   file-open "Data/govparty.txt" let patch-gp file-read file-close
   (foreach sort patches patch-gp
-     [ ask ?1 [ set layer-govparty ?2 ] ] )
+     [ [?1 ?2] -> ask ?1 [ set layer-govparty ?2 ] ] )
 
   file-open "Data/govyear.txt" let patch-gyr file-read file-close
   (foreach sort patches patch-gyr
-     [ ask ?1 [ set layer-govyear ?2 ] ] )
+     [ [?1 ?2] -> ask ?1 [ set layer-govyear ?2 ] ] )
 
   file-open "Data/govvote.txt" let patch-gvote file-read file-close
   (foreach sort patches patch-gvote
-     [ ask ?1 [ set layer-govvote ?2 ] ] )
+     [ [?1 ?2] -> ask ?1 [ set layer-govvote ?2 ] ] )
 
   file-open "Data/govtwyear.txt" let patch-gty file-read file-close
   (foreach sort patches patch-gty
-     [ ask ?1 [ set layer-govtwyear ?2 ] ] )
+     [ [?1 ?2] -> ask ?1 [ set layer-govtwyear ?2 ] ] )
 
   file-open "Data/govfol.txt" let patch-gf file-read file-close
   (foreach sort patches patch-gf
-     [ ask ?1 [ set layer-govfol ?2 ] ] )
+     [ [?1 ?2] -> ask ?1 [ set layer-govfol ?2 ] ] )
 
   file-open "Data/sparty.txt" let patch-sparty file-read file-close
   (foreach sort patches patch-sparty
-     [ ask ?1 [ set layer-sparty ?2 ] ] )
+     [ [?1 ?2] -> ask ?1 [ set layer-sparty ?2 ] ] )
 
   file-open "Data/svote.txt" let patch-sv file-read file-close
   (foreach sort patches patch-sv
-     [ ask ?1 [ set layer-svote ?2 ] ] )
+     [ [?1 ?2] -> ask ?1 [ set layer-svote ?2 ] ] )
 end
 
 to setup-governors
@@ -274,8 +273,8 @@ end
 GRAPHICS-WINDOW
 210
 10
-1106
-416
+1104
+394
 -1
 -1
 1.5
@@ -304,7 +303,7 @@ BUTTON
 131
 43
 NIL
-setup\n
+setup
 NIL
 1
 T
@@ -359,7 +358,7 @@ PLOT
 420
 289
 585
-population
+population_1
 time
 population
 0.0
@@ -444,7 +443,7 @@ chances-to-change-mind
 chances-to-change-mind
 0
 5
-2
+2.0
 1
 1
 NIL
@@ -490,7 +489,7 @@ PLOT
 421
 590
 583
-population
+population_1
 time
 Count
 0.0
@@ -528,7 +527,7 @@ decay-after-times-heard
 decay-after-times-heard
 0
 72
-36
+36.0
 12
 1
 NIL
@@ -934,9 +933,8 @@ false
 0
 Polygon -7500403 true true 270 75 225 30 30 225 75 270
 Polygon -7500403 true true 30 75 75 30 270 225 225 270
-
 @#$#@#$#@
-NetLogo 5.3.1
+NetLogo 6.1.0
 @#$#@#$#@
 @#$#@#$#@
 @#$#@#$#@
@@ -952,7 +950,6 @@ true
 0
 Line -7500403 true 150 150 90 180
 Line -7500403 true 150 150 210 180
-
 @#$#@#$#@
 0
 @#$#@#$#@

CSS645Models/Information_Propagation /README.md

@@ -4,8 +4,6 @@
 
 "Understanding the diffusion of information among a certain type of population will provide insights into how quickly this population will receive the information, and how effective such diffusion was for spreading the message. Social media has enabled the information to spread at a relatively lower cost and greater timeliness, comparing to traditional ways such as face-to-face contact. To approach such phenomenon at individual-based level, which preserves the heterogeneity among the population, this study employed an agent-based model to explore information propagation in physical and cyber spaces. Specifically, I focused on the information diffusion patterns among opinionated groups of people. Therefore, a study case was established, which investigated the propagation of the messages sent by a number of governors of U.S. among the electors that belong to different parties. Empirical data were used for model initialization and some parameter settings. Scenario analysis was applied for detecting the patterns of information propagation under different conditions. The key finding is that generally, cyber world information propagation has a noticeable positive influence on its physical world propagation. However when information decay and noise are also considered, its influence becomes less obvious. Besides, the information propagation patterns in different scenarios are successfully captured."
 
-**Keywords**: Agent-based modeling, Twitter, information propagation
-
 ##  
 ![Agents Status](Agents_status.png)
 

CSS645Models/LARPing/README.md

@@ -4,8 +4,6 @@
 
 "The National Institute of Justice defines recidivism as a person’s relapse into criminal behavior, often after the person undergoes intervention for a previous crime.  Many researchers have found that the implementation of post-incarceration reentry programs has a significant impact on recidivism rates at both city and state-wide scales. This paper applies an agent-based modeling simulation to examine the potential impact of multiple reentry programs, used in tandem, on newly-released offenders, using the District of Columbia’s publicly available crime data. Results of this model indicate that layering access to reentry programs substantially lowers both the recidivism rate of criminal offenders and the average number of times offenders are arrested within their lifetimes (on average 16% and 7% respectively). This research demonstrates the potential and limitations of using agent-based modeling to inform public policy decisions and budget priorities with regards to criminal offender reentry programs."
 
-**Keywords**: agent-based modeling, GIS, recidivism, reentry programs
-
 ##  
 UML for ABM:
 ![UML](UML.png)

CSS645Models/MARCH_ON/MARCH-ON.nlogo

@@ -1,5 +1,3 @@
-;Modeling Appalachian Trail Recreation: Camping, Hiking & Overcrowding in our Natural parks (MARCH-ON)
-
 
 globals [
  shelters

CSS645Models/MARCH_ON/README.md

@@ -4,7 +4,6 @@
 
 "The Appalachian Trail (AT) is America’s most famous hiking trail, but increasing visitor numbers put pressure on campsites and threatens the AT’s ecosystem and character. AT hikers are strongly encouraged to use designated campsites to minimize their impact on the environment, but hikers frequently engage in “unregulated” camping where no designated campsite is available. Within this paper we introduce MARCH-ON, an agent-based model which simulates AT hiker movement and the camping decision-making process to identify the areas of the AT in Virginia most at-risk for cumulative environmental damage caused by unregulated camping. MARCH-ON seeks to combine accurate GIS data, AT hiker survey data, Tobler’s hiking function, and agent-based modeling concepts to model the dynamics of hiker behavior and the spatial relationships of campsite locations along the AT. MARCH-ON demonstrates that hikers tend to camp in unregulated “bootleg” campsites when the distance between designated campsites is large, and that inexperienced hikers are most likely to engage in unregulated camping. Findings indicate that the northeastern portion of the AT in Virginia, including Shenandoah National Park, is most at-risk for damage from unregulated camping."
 
-**Keywords**: Agent-based modeling, Tobler’s hiking function, Appalachian Trail, bootleg campsites, GIS.
 
 ##