Presentation slides. Carlos A. Vanegas, Daniel G. Aliaga, Bedrich Benes, Paul Waddell, “Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling”, ACM Transactions on Graphics (Proceedings SIGGRAPH Asia), 28(5), 2009.

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2. Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling Carlos Vanegas1 Daniel Aliaga1 Bedřich Beneš2Paul Waddell3 1Computer Science, Purdue University, USA 2Computer Graphics Technology, Purdue University, USA 3City and Regional Planning, UC Berkeley, USA 2

4. Urban visualization

5. Policy evaluation and education

6. Content generation for games and movies3

7. Examples (1) Designer increases height of buildings in downtown Number of jobs increases Population increases New housing appears in accessible areas 4

8. Examples (2) Designer inserts a highway into the model Accessibility increases in formerly remote areas Population redistributes Location of jobs, houses, and buildings changes 5

9. Observation Reduction of design time Production of plausible urban models Behavioral Modeling Geometrical Modeling 6

11. Blocks

12. Parcels

14. Jobs

15. Accessibility

16. Land ValueUser 7

18. Blocks

19. Parcels

21. Jobs

22. Accessibility

23. Land Value8

25. Blocks

26. Parcels

28. Jobs

29. Accessibility

30. Land ValueUser 9

32. Blocks

33. Parcels

35. Jobs

36. Accessibility

37. Land ValueUser 10

39. Müller et al., 2001, 2006

40. Wonka et al., 2003

41. Chen et al., 2008

42. Aliaga et al., 200811 Geometrical Modeling Behavioral Modeling

44. Sung et al., 2004

45. Treuille et al., 2006

46. Flocking and animal behavior modeling

47. Reynolds., 1987

48. Tu and Terzopoulos., 1994 12 in Graphics Geometrical Modeling Behavioral Modeling

50. Microsimulation

51. Uses discrete-choice models

52. Uses agents that make decisions to locate and move within urban space

53. e.g., UrbanSim [Waddell et al., 2002]13 in Urban Simulation Geometrical Modeling Behavioral Modeling

55. Weber et al., 2009

56. Visualization of Simulated Cities

57. Vanegas et al., 2009 Behavioral Modeling Do not focus on producing interactive design/editing time and ensuring plausible 3D urban models

59. Urban Design as a Dynamical System

60. Behavioral Modeling

61. Geometrical Modeling

62. Results15

64. Consists of N variables defined over a spatial domain

65. Each variable sampled over a 2D spatial grid G of sizeWx H

66. vk(i,j) denotes the value ofk-thvariable at grid cell (i,j)j Grid i v0(i,j), v1(i,j), … , vN (i,j) 16 H W

68. Job count

69. Accessibility

70. Land value

71. Road length

72. Building volume

73. Average tortuosity

75. The change in each variable vk(i,j)is represented as a differential equation

76. Iftheuserchanges a variable, thesystemiterativelyupdatesallother variables in ordertoreturnto a state of equilibrium18

81. Blocks

82. Parcels

84. Jobs

85. Accessibility

86. Land ValueUser 22

88. Blocks

89. Parcels

91. Jobs

92. Accessibility

93. Land ValueUser 23

95. Population count

96. Job count

97. Accessibility

98. Land value24

100. Operations:

101. Remove a fraction of population/jobs from their current location (Mobility algorithm)

102. Locate population/jobs to predicted locations (Location choice algorithm)

103. Each unit of population/jobs is referred to as an agent25

105. Agents de-located from grid are moved to queue Q

106. Agents added by user are moved to queue QGrid User addspopulation/jobs 26

108. Agents from queue are placed back in the gridGrid 27

110. Uses weighted attractiveness measure as a probability

111. The probability qstthat an agent es will locate at the grid cell (it, jt) is given byat :accessibility at the grid celllt : land value at the grid cellTs : total count of the agent throughout the entire grid28

113. Measure of access that a grid cell has to jobs and to the rest of the population

114. Intuitively

115. Decreases with increasing terrain slope

116. Increases with higher road connectivity and nearby population/jobs29

118. Represented by logistic function

119. u1: proximity to highways, arterials and streets

120. u2: local slope of the terrain

121. u3: Distance-normalized measure of activity level at (i,j) 30

123. Blocks

124. Parcels

126. Jobs

127. Accessibility

128. Land ValueUser 31

130. Road length

131. Average tortuosity

132. Building volume

133. Terrain elevation at grid cell (user-controlled)32

135. Total length of roads in grid cell

136. Two types: Arterials and Streets

137. Average Tortuosity

138. Ratio between road segment length and distance between segment endpoints33

140. To connect the main population clusters a set of seeds is generated considering the population/jobs distribution and the location of highways34

142. Each seed is used as an intersection of the arterial roads network and used to generate arterial segments35

144. Street seeds are generated along arterial road segments and used to create streets36

146. Using the seeds positioned according to population/jobs and/or along arterials, we generate road segments using a breadth-first expansion method

147. These seeds are placed into a queue37

158. Geometrical Modeling Grid Radial Tortuosity 48

160. Total volume of all the buildings in grid cell

161. Computed as a function of population and jobs

162. To generate a building geometry that matches the volume, we first generate parcels and building footprints49

164. Blocks are extracted from the road network and partitioned into parcels

165. The number of parcels in the block is proportional to the product of the area of the block and the count of population/jobs in the grid cells inside the block50

168. Procedurally generated inside each parcel based on the socioeconomic information of the area

169. Process:

170. Calculate geometry of the building footprint

171. Calculate building height

172. Use procedural rules to generate 3D geometry that matches these attributes52

174. Blocks

175. Parcels

177. Jobs

178. Accessibility

179. Land ValueUser 53

181. Grid-cell size 0.1x0.1 km

182. Grid size up to 50x50 km in our experiments

183. Time:

184. Update time step during editing:

185. <0.3 seconds for small grid (5x5 km)

186. <4 seconds for large grids (15x15 km)

187. Total Design Time:

188. <5 minutes for any of our examples54

189. Results: Stability 55 Terrain and Roads Equilibrium Equilibrium

190. Results: Terrain Editing 56 Example 1 Example 2 Lake Mountains Initial

191. Results: Completion and Validation (1) 57 Town center Population Parcels Parks Jobs Buildings Terrain Input Output

192. Results: Completion and Validation (2) 58 Real City Synthetic City

194. Our key inspiration is to close the loop between behavioral modeling and geometrical modeling producing a single dynamical system that assists a designer in creating urban models59

196. Stochastic component that does not allow behaviors and geometries to be exactly repeatable

197. Over-constraining the system can lead to unfeasible urban models60

199. Adding methods to generate more complex geometric structures using socioeconomic data (e.g., additional building details such as facades)61

201. Aaron Link for his help with exporting models

202. Students from the Computer Graphics and Visualization Lab at Purdue for their feedback

203. Reviewers for their valuable comments and suggestions62

204. Thank you! 63