The document presents a study on the relationship between urban environment attributes and pedestrian movement in two areas of Tel Aviv. Key findings indicate that connectivity by street name, total commercial front, and residential density significantly correlate with pedestrian volume, with spatial-physical attributes generally showing greater correlation than functional attributes. Notably, the eastern area displays different correlations compared to the western area, largely influenced by the presence of a large square.

1. Morphological and Functional
Attributes of the Urban Environment
and Pedestrian Movement
Presented by: Yoav Lerman
Tel-Aviv University

2. The joy of being a pedestrian

3. The sorrow of being a pedestrian

4. Tel-Aviv Basics
Founded: 1909
Population: 400,000
Land size: 52 sq. km
Metro Population: 3 million

10. Agenda
Research question
Research area location
Methodology
Spatial-physical dimension
Functional dimension
Findings

11. Research Question
Which attributes of the built environment correlate
with the volume of pedestrian movement in two
adjacent areas in the center of Tel-Aviv?

12. Research Area

13. Research Area
East of Ibn-Gvirol street vs. west of Ibn-Gvirol street
Research area boundary
Sub areas boundary

14. Methodology
 Dependent variable: pedestrian counts
 Independent variables: built environment
attributes
 Positivist methodology based on non-intrusive
observations
 Looking for statistical correlations between the
independent variables and the dependent
variable

15. Each square – 500m X 500m

16. Two dimensions of the built
environment
 Spatial-physical dimension
The basis of the urban form
Extremely durable and rarely modified
 Functional dimension
The content that fills the form
Relatively fast changes

17. Spatial-Physical Variables
 Space syntax measures
 Connectivity by street name
 Pavement width
 Road crossing difficulty
 Intersection density

18. Functional Variables
 Commercial fronts
 Residential density
 Proximity to bus stations

19. Space Syntax Measures
Use of DepthMap software based upon axial lines
analysis:
Connectivity
Control
Integration
- Global Integration – Mean distance from the entire street network
- Local Integration – Mean distance from nearby streets

20. A Comment about Mapping
 Fixed the street network according to
pedestrian routes
Boulevards
Squares

21. Street Scheme

22. Axial Lines

23. Connectivity

24. Connectivity

25. Global Int.

26. Global Int.

27. Connectivity by Street Name

28. Pavement Width

29. Commercial
Fronts

30. Pedestrian Count Points
 95 count points
 51 street segments
24 western segments
24 eastern segments
3 border segments
 Count method:
5 minutes at each point
5 counts at each point
(once per hour for 5
hours)

31. Pedestrian Count Points Location

32. Avg. Pedestrian Volume in each
segment (per hour)

33. Findings
 Four correlated variables in descending order:
R squared 0.83
1. Connectivity by street name
2. Total commercial front
3. Residential density in subzone
4. Proximity to bus stations

34. Findings – Western Area
 One correlated variable
 Connectivity by street name
R squared 0.82
R squared 0.88 without boulevards and squares

35. Findings – Eastern Area
 Three correlated variables:
R squared 0.86
1. Total commercial front
2. Space syntax connectivity
3. Space syntax control

36. Findings – Eastern Area (Cont’)
 Without the squares (Kikar Hamdina)
 Three correlated variables:
R squared 0.9
1. Connectivity by street name
2. Space syntax global int.
3. Total commercial front

37. Summary
 In most cases the spatial-physical structure has
greater correlation than the functional structure
with pedestrian movement
 There are major differences between the
western and eastern areas correlations
 Connectivity by street name correlated better
than space syntax variables
 The large square in the eastern side changes
the correlation model significantly