Morphological and Functional

Attributes of the Urban Environment

and Pedestrian MovementPresented by: Yoav Lerman

Tel-Aviv University

The joy of being a pedestrian

The sorrow of being a pedestrian

Tel-Aviv Basics

Founded: 1909

Population: 400,000

Land size: 52 sq. km

Metro Population: 3 million

Agenda

Research question

Research area location

Methodology

Spatial-physical dimension

Functional dimension

Findings

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?

Research Area

Research Area

East of Ibn-Gvirol street vs. west of Ibn-Gvirol street

Research area boundary

Sub areas boundary

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

Each square – 500m X 500m

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

Spatial-Physical Variables

Space syntax measures Connectivity by street name Pavement width Road crossing difficulty Intersection density

Functional Variables

Commercial fronts Residential density Proximity to bus stations

Measures Space Syntax

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

A Comment about Mapping

Fixed the street network according to pedestrian routes Boulevards Squares

Street Scheme

Axial Lines

Connectivity

Connectivity

Global Int.

Global Int .

Connectivity by Street Name

Pavement Width

Commercial Fronts

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)

Pedestrian Count Points Location

Avg. Pedestrian Volume in each segment (per hour)

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

Findings – Western Area

One correlated variable Connectivity by street name

R squared 0.82 R squared 0.88 without boulevards and squares

Findings – Eastern Area

Three correlated variables: R squared 0.86

1. Total commercial front

2. Space syntax connectivity

3. Space syntax control

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

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