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challenges facing express delivery services in canada's urban centres

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Institute of Hous<strong>in</strong>g & Mobility<br />

Challenges <strong>fac<strong>in</strong>g</strong> the EDS <strong>in</strong>dustry <strong>in</strong> Canada's <strong>urban</strong> <strong>centres</strong><br />

Overlay<strong>in</strong>g park<strong>in</strong>g tickets with <strong>delivery</strong> data<br />

The project team obta<strong>in</strong>ed park<strong>in</strong>g ticket data from the City of Toronto to determ<strong>in</strong>e the high<br />

concentration zones for park<strong>in</strong>g tickets <strong>in</strong> Toronto dur<strong>in</strong>g 2008. We obta<strong>in</strong>ed data for all<br />

addresses or locations where the ticket<strong>in</strong>g authorities issued <strong>in</strong> excess of 1,000 tickets. The<br />

park<strong>in</strong>g ticket data were geo-coded before be<strong>in</strong>g analyzed us<strong>in</strong>g GIS. Figure 33 presents the<br />

spatial distribution of the park<strong>in</strong>g tickets, overlaid with the spatial distribution of the courier<br />

deliveries <strong>in</strong> the downtown area for December 2007. The p<strong>in</strong>k dots are scaled to represent the<br />

number of park<strong>in</strong>g tickets issued. It could be seen from Figure 33 that there is a strong overlap<br />

between the high volume of deliveries and multiple locations where more than 1,000 park<strong>in</strong>g<br />

tickets were issued <strong>in</strong> the area bounded by Bay Street to the east, York Street to the West, Front<br />

Street to the south and Queen Street to the north.<br />

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Yonge and K<strong>in</strong>g<br />

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Market_Dec_Pkgs<br />

999 and below (31)<br />

1000 to 1999 (13)<br />

2000 to 3999 (8)<br />

4000 to 7999 (4)<br />

8000 and above (2)<br />

Num_Tickets<br />

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10000 5000 2500<br />

0 .5 1 1.5<br />

Kilometers<br />

FIGURE 33: OVERLAYING PARKING TICKET DATA WITH DELIVERY DATA<br />

S<strong>in</strong>ce the <strong>delivery</strong> data were obta<strong>in</strong>ed for a week <strong>in</strong> four different months, the project team also<br />

estimated daily differences <strong>in</strong> <strong>delivery</strong> patterns. It appears that the <strong>delivery</strong> activity usually picks<br />

up over the course of a week where Monday represents lower volume than other days of the<br />

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