Setembre 2014
Taming the road
Housing + housing mixt
Specialized
Natural productive
Natural non-productive
Housing + Housing mixt + Specialized
Natural non productive + Natural productive
Road network: all
Road network: paved
Road network: non-paved
Road network: non-secreted
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
viatges/m2s
viatges/m2st
m2st
m2s
viatges
Decret mobilitat + Trip generation Manual. Aproximadament idea (“pesos”) de intensistat/densitat de l’activitat
m2st/m2s (1 si és sòl)
Trip generation based
180_Centre urbà 181_Eixample 182_Habitatges unifamiliars 179_Urbanitzacions 183_Colònies i nuclis aïllats 184_Cases aïllades
7,00 7,00 7,00 7,00 7,00 7,00
80,00 90,00 120,00 140,00 90,00 140,00
0,09 0,08 0,06 0,05 0,08 0,05
2,65 1,50 0,35 0,35 2,00 0,35
0,2319 0,1167 0,0204 0,0175 0,1556 0,0175
185_Polígon industrial ordenat 186_ 187_Indústries aïllades
5,00 5,00 5,00
100,00 100,00 100,00
0,05 0,05 0,05
0,50 0,50 0,50
0,0250 0,0250 0,0250
189_Complexos comercials i d'oficines
20,00
100,00
0,20
1,00
0,2000
213_Complexos administratius 214_Equipaments sanitaris 215_centres educatius 216_centres penitenciaris 217_centres religiosos 218_centres culturals 195_cementiris
15,00 20,00 20,00 20,00 20,00 20,00 0,00
100,00 100,00 100,00 100,00 100,00 100,00
0,15 0,20 0,20 0,20 0,20 0,20
1,00 1,00 0,90 0,90 0,90 0,90
0,1500 0,2000 0,1800 0,1800 0,1800 0,1800
1,00
0,0012
cemetery
R1 land use-based
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
Gini–Simpson index The original Simpson index λ equals the probability that two entities taken at random from the dataset of interest (with replacement) represent the same type. Its transformation 1 − λ therefore equals the probability that the two entities represent different types. This measure is also known in ecology as the probability of interspecific encounter (PIE) and the Gini–Simpson index. It can be expressed as a transformation of true diversity of order 2:
The Gibbs–Martin index of sociology, psychology and management studies,[11] which is also known as the Blau index, is the same measure as the Gini–Simpson index.
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
Distance to Road network: non-secreted
Distance to Train Station : 0-1000; 1000-2000;>2000
Distance to Train Station : 0-2000; 2000-5000;>5000
Distance to Train Station : 0;0-2000;>2000
Distance to Train Station : 0;0-2000;>2000
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
W[i] +
Shortest path tree
Reach
>
Upstream visualization
R= 300 m
R= 300 m
R= 300 m
R= 300 m
R= 300 m
R= 300 m
R= 500 m
R= 500 m
R= 500 m
R= 500 m
R= 500 m
R= 500 m
R= 1.000 m
R= 1.000 m
R= 1.000 m
R= 1.000 m
R= 1.000 m
R= 1.000 m
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
R= 1.000 m
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
Gini–Simpson index The original Simpson index λ equals the probability that two entities taken at random from the dataset of interest (with replacement) represent the same type. Its transformation 1 − λ therefore equals the probability that the two entities represent different types. This measure is also known in ecology as the probability of interspecific encounter (PIE) and the Gini–Simpson index. It can be expressed as a transformation of true diversity of order 2:
The Gibbs–Martin index of sociology, psychology and management studies,[11] which is also known as the Blau index, is the same measure as the Gini–Simpson index.
R= 1.000 m
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
Animation
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
B; R= 500 m
B; R= 500 m
B; R= 1000 m
B; R= 2000 m
B; R= 5000 m
B; R= 10.000 m
B; R= global
C; R= 300 m
C; R= 500 m
C; R= 1.000 m
C; R= 2.000 m
C; R= 5.000 m
C; R= 10.000 m
C; R= global
R; R= 300 m
R; R= 500 m
R; R= 1.000 m
R; R= 2.000 m
R; R= 5.000 m
R; R= 10.000 m
R; R= global
S; R= 300 m
S; R= 500 m
S; R= 1.000 m
S; R= 2.000 m
S; R= 5.000 m
S; R= 10.000 m
S; R= global
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
Activity trip-generation based
Kernel density B: 300, 500, 1000, 2000, 5000, 10000
Kernel density C: 300, 500, 1000, 2000, 5000, 10000
Kernel density R: 300, 500, 1000, 2000, 5000, 10000
Kernel density S: 300, 500, 1000, 2000, 5000, 10000
ols
predictor Reach
Betweenness
Straightness
Closeness ĂŠs l'invertit
search radius 300 500 1000 2000 5000 10000 infinite 300 500 1000 2000 5000 10000 infinite 300 500 1000 2000 5000 10000 infinite 300 500 1000 2000 5000 10000 infinite
kernel tg_total absolute 0,8315 0,8502 0,8703 0,7852 0,5108 0,3978 0,3628 0,7961 0,8462 0,8740 0,8144 0,5466 0,3102 0,1223 0,8278 0,8496 0,8744 0,7987 0,5385 0,4197 0,3996 0,8356 0,8550 0,8705 0,7277 0,4127 0,3543 0,2764
area/ segment length tg_total 0,1270 0,1999 0,2258 0,1970 0,1050 0,0708 0,0427 0,0008 0,0405 0,0821 0,0565 0,0141 0,0034 0,0007 0,1053 0,1869 0,2197 0,1990 0,1144 0,0790 0,0539 0,1829 0,2104 0,2152 0,1563 0,0646 0,0519 0,0213
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
Housing mixt. R=1.000 m
Housing monoculture. R= 1.000m
Industrial park. R= 1.000 m
Comercial + Offices Park. R= 1.000 m
Hotel complex + Camping. R= 1.000 m
Esports + Parcs Urbans. R= 1.000 m
Equipaments. R=1.000 m
Agriculture. R= 1.000 m
Forest. R= 1.000 m
Water. R= 1.000 m
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
creuaments nous 5x6
2D: Diversity + BT 2000
3D: Diversity + Density + Centrality BT 2000
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
Queries
1000 DENSITAT DESITJADA
25 hab ha
1 ha 10000 m2
3141590 m2
g101
7853,975 hab
100 m2st 1 hab
g102
7853,975 hab
100 m2st 1 hab
g103
7853,975 hab
150 m2st 1 hab
1 m2s 0,35 m2st/m2s
g104
7853,975 hab
150 m2st 1 hab
1 m2s 0,35 m2st/m2s
g101
1 hab
100 m2st 1 hab
g102
1 hab
g103
g104
7.854 habitatges en un radi de 1km
1 m2s 296.376 m2sòl mínim de massa crítica 2,65 m2st/m2s
0,08805
0,09
1 m2s 523.598 m2sòl mínim de massa crítica 0,155556 1,5 m2st/m2s
0,15
3.365.98 9 m2sòl mínim de massa crítica
1
1
3.365.98 9 m2sòl mínim de massa crítica
1
1
1 m2s 2,65 m2st/m2s
38 m2sòl mínim de massa crítica 1,12E-05
0,09
100 m2st 1 hab
1 m2s 1,5 m2st/m2s
67 m2sòl mínim de massa crítica 1,98E-05
0,15
1 hab
150 m2st 1 hab
1 m2s 0,35 m2st/m2s
429 m2sòl mínim de massa crítica 0,000127
1
1 hab
150 m2st 1 hab
1 m2s 0,35 m2st/m2s
429 m2sòl mínim de massa crítica
1
1
SELECTION ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 0 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 250 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 1000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 2000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 4000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 15000
QUERY H1 H2 H3 H4 H5 H6 H7
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 300000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 100000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 50000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 25000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 10000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 2000
H3_I1 H3_I2 H3_I3 H3_I4 H3_I5 H3_I6
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 50000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 25000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 12500 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 6000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 3000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 1000
H3_T1 H3_T2 H3_T3 H3_T4 H3_T5 H3_T6
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 250000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 125000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 60000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 30000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 15000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 5000
H3_AI1 H3_AI2 H3_AI3 H3_AI4 H3_AI5 H3_AI6
SELECTION ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 0 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 250 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 1000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 2000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 4000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 15000
QUERY H1 H2 H3 H4 H5 H6 H7
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 300000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 100000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 50000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 25000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 10000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 2000
H3_I1 H3_I2 H3_I3 H3_I4 H3_I5 H3_I6
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 50000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 25000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 12500 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 6000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 3000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 1000
H3_T1 H3_T2 H3_T3 H3_T4 H3_T5 H3_T6
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 250000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 125000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 60000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 30000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 15000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 5000
H3_AI1 H3_AI2 H3_AI3 H3_AI4 H3_AI5 H3_AI6
SELECTION ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 0 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 250 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 1000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 2000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 4000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 15000
QUERY H1 H2 H3 H4 H5 H6 H7
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 300000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 100000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 50000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 25000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 10000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 2000
H3_I1 H3_I2 H3_I3 H3_I4 H3_I5 H3_I6
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 50000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 25000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 12500 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 6000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 3000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 1000
H3_T1 H3_T2 H3_T3 H3_T4 H3_T5 H3_T6
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 250000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 125000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 60000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 30000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 15000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 5000
H3_AI1 H3_AI2 H3_AI3 H3_AI4 H3_AI5 H3_AI6
SELECTION ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 0 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 250 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 1000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 2000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 4000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 15000
QUERY H1 H2 H3 H4 H5 H6 H7
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 300000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 100000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 50000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 25000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 10000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 2000
H3_I1 H3_I2 H3_I3 H3_I4 H3_I5 H3_I6
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 50000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 25000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 12500 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 6000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 3000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 1000
H3_T1 H3_T2 H3_T3 H3_T4 H3_T5 H3_T6
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 250000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 125000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 60000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 30000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 15000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 5000
H3_AI1 H3_AI2 H3_AI3 H3_AI4 H3_AI5 H3_AI6
SELECTION ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 0 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 250 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 1000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 2000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 4000 ("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 15000
QUERY H1 H2 H3 H4 H5 H6 H7
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 300000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 100000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 50000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 25000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 10000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g201" > 2000
H3_I1 H3_I2 H3_I3 H3_I4 H3_I5 H3_I6
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 50000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 25000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 12500 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 6000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 3000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g301" > 1000
H3_T1 H3_T2 H3_T3 H3_T4 H3_T5 H3_T6
(("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 250000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 125000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 60000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 30000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 15000 (("g101"/40 + "g102"/70+ "g103"/450+ "g104"/450) > 500 ) AND "g1101" > 5000
H3_AI1 H3_AI2 H3_AI3 H3_AI4 H3_AI5 H3_AI6
CASES
CH 5
METHODS
RESULTS
DISCUSSION 1
DICUSSION 2
CH 4
CH 5, 6, 7
CH 5, 6, 7
CH 8, 9
CH 9
M
R
D
C
C
Visualizing
M1 Voronoi-based
R1 land use-based
activity
micro-catchement areas
C1 MACRO NODES R2 trip generation-based
dibuxiar rodones als valors
M2 downstream visualization based on catchement areas
alts de trip g i de diversitat R3 diversity-based
tb fer-ho en relació al r4 ono
R4 distance-based D1 Activity cells identification M3 upstream visualization
R4 land use-based
based on reach/shortest path tree R5 trip generation-based
C3 "TAMING THE ROAD"
housing-mixed vs mono-culture working industrial
smart-table
working terciari
strategies i intervencions along the road smartgrowth
working regadiu-secà R6 diversity-based
natural no productiu DX Density
R7 route animation-based
3d_gini D2 Diversity cells identification
CH 6
centrality
M4 centrality measures
R8 centrality and search radius
measures
D3 Micro centralities identification bt 500-1000 bt 2000-tendencia del 5000
CH 7
Correlation
M5 segment density + R2
R9 correlation
M6 kernel density + R2
R10 correlation
Centrality-Activity
C2 MICRO STRUCTURES
D4 Centrality measures* validation escriure les bondats de la centralitat com a predictor de localització d'activitat
Queries
STRATEGIES PARAMETERS
VALUES
DOWNSTREAM
REACH HOUSING MIX
DIVERSITY
ACTIVITY DENSITY
CENTRALITY
TRAIN STATION
HIGH WAY
PEIN
IMD
PLANNING UNIT
NONE HIGH NONE HOUSING MIX H2 H3 H4
1
I
0- NAT BREAK NAT BREAK - NAT BREAK NAT BREAK - 1
LOW MEDIUM HIGH
I
0- NAT BREAK NAT BREAK - NAT BREAK NAT BREAK - 1
LOW MEDIUM HIGH
I
0- NAT BREAK NAT BREAK - NAT BREAK NAT BREAK - 1
LOW MEDIUM HIGH
I
0 - 1000 m 1000 - 2000 m > 2000 m
0 1 2
I
0 - 2000 2000 - 5000 > 5000
0 1 2
I
0m 0 - 2000 > 2000
0 1 2
I
0 - 1000 v
LOW LOW MEDIUM MEDIUM MEDIUM HIGH HIGH VERY HIGH
I
MICRONODALITY
D1
INTERVENCIONS D2
D3
WALKABILITY
TRAFFIC CALMING
RETROFITTING
Taming the ROAD 1. Introduction. Introducció 2. Related Works: Towards an intermediate urban mosaic. Vers un Mosaic Urbà Intermedi 2.1.Vers la decantació d’un Mosaic Urbà Intermedi 2.1.1.El mosaic urbà intermedi 2.1.2.La xarxa viària intermèdia com a element central del medi urbà dels fragments 2.1.3.Les forces descentralitzadores de la dispersió i la metropolinització 2.1.4.Evidències sobre la necessitat d’una (re)construcció del mosaic urbà intermedi. 2.2.L’emergència del Mosaic Urbà Intermedi en el marc del “cicle de vida urbà” 2.2.1.Caracterització del cicle de vidà urbà 2.2.2.L’aparició del Mosaic territorial Intermedi en les etapes de sub-urbanització i contra-urbanització 2.3.Lectura del mosaic urbà intermedi des d’una aproximació transversal als espais oberts 2.3.1.La percepció com a mitjà de reconeixement dels espais oberts 2.3.2.La morfologia dels intersticis 2.3.3.La necessitat d’una interacció amb el “verd” proveïdor de serveis socials 2.3.4.Els serveis ecològics del mosaic territorial i llurs limitacions en el mosaic urbà del viari intermedi 2.3.5.Vers una activació transversal del espais oberts 2.4.La caracterització del mosaic urbà intermedi a través de patrons de localització d’activitats des d’una perspectiva morfològica. Alguna cosa d’informació STOCKS 2.4.1.Cartografia i Taxonomia del medi urbà 2.4.2.La identificació dels mecanismes de producció del mosaic urbà intermedi i la seva relació amb la xarxa viària intermèdia 2.5.La caracterització del Mosaic Urbà Intermedi a través dels patrons relacionals espacials en el marc de la informació. Vers una aproximació morfotopològica FLOWS crowdsourcing, agent-based, altres..., tema anàlisi espaial 2.5.1.Evolució del binomi “ciutat-transport/mobilitat” 2.5.2.La identificació de patrons relacionals espacials 2.5.3.Vers una lectura morfotopològica de la ciutat dels fragments servida per la xarxa viària intermèdia 2.6.Vers una lectura morfotopològica de la ciutat dels fragments servida per la xarxa viària intermèdia
3. Problem Statement. Problema 4. Data and Methods. Dades i Mètodes 4.1.Data 4.1.1.Land Covers 4.1.2.Road Network 4.1.3.Altres ptmb, pein, ‌. 4.2.Methodology 4.2.1.Voronoi-based on land use micro-catchement areas (M1) 4.2.2.Downstream visualization based on land use catchement areas (M2) 4.2.3.Upstream visualization based on catchement areas (M3) 4.2.4.Centrality measures (M4) 4.2.5.Activity-density/Centrality measures correlation (M5, M6)
5.
Visualizing land uses/ road-network/ proximity scale. Visualització de les estructures d’activitat de proximitat que suporta la xarxa viària intermèdia
5.1.Data preparation. Prepacració de dades 5.2.“Downstream” activity on the road visualization. Visualització de l’activitat de proximitat sobre la xarxa viària “aigüesavall”. 5.3.“Upstream” activity visualization. Visualització de l’activitat de proximitat sobre la xarxa viaria “aigüesamunt”. 5.4.On the road activity Animation 5.5.Classification 5.6.Results 5.7.Summary / Discussion
6. Identification of / centrality measures. La Identificació dels potencials àmbits supramunicipals d’activitat mitjançant les mesures de centralitat
6.1.Data preparation 6.2.Reach centrality 6.3.Closeness centrality 6.4.Betweenness centrality 6.5.Straightness centrality 6.6.Results 6.7.Summary / Discussion
7. OLS Correlation. Les mesures de centralitat com a predictors de les estructures d’activitat de proximitat 7.1.Setting dependant variables. L’establiment de variables dependents 7.2.Setting Independant variables. L’establiment de variables independents 7.3.Correlation OLS, ODS ..... Ordinary Linear... 7.4.Results. Resultats 7.5.Summary / Discussion. Discussió
8. Taming the road: . Domesticant la carretera: La delimitació i planejament dels àmbits territorials de proximitat del mosaic urbà intermedi 8.1.Macro Nodes (C1). L’establiment i delimitació d’enclavaments estratègics en el si dels territoris supramunicipals de proximitat segons llur naturalesa i potencial endògen 8.2.Micro Structures (C2). L’establiment i delimitació d’àmbits per a l’ordenament i projecte dels territoris supramunicipals segons llurs naturalesa i potencial endògen 8.3.Strategies and interventions along the road for proximity smartgrowth territories (C3). Estratègies per a l’ordenament i projecte dels territoris de proximitat d’abast supramunicipal per a la (re)construcció del mosaic urbà intermedi
9. Discussion 9.1.Research Summary. Sumari de la recerca 9.2.Contributions. Contribucions principals per a una visualitzaci贸, delimitaci贸 i planejament del territoris supramunicipals de proximitat mecanitzada i no mecanitzada 9.3.Limitations. Limitacions 9.4.Future Research Directions. Futures l铆nies de recerca Glossary Appendix Appendix A: Publications Appendix B: Used Datasets: Appendix C: Code and Processes References