Taxis Are Our Friends Mapping the “taxi-friendliness” of neighborhoods in the Westside of Los...
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Taxis Are Our Friends Mapping the “taxi-friendliness” of neighborhoods in the Westside of Los Angeles County Earl Kaing UP206A – Intro to GIS 12/6/2011
Taxis Are Our Friends Mapping the taxi-friendliness of
neighborhoods in the Westside of Los Angeles County Earl Kaing
UP206A Intro to GIS 12/6/2011 Final Presentation Source: Earl
Kaing
Slide 2
INTRODUCTION Source: D.L. Scrimger
Slide 3
The Urban Agenda Once someone is forced to buy a car, its all
over: the private automobile is a huge investment; and once you
sink money into that investment, the marginal costs (both real and
perceived) of driving are almost negligible. In other words, when
you own a car, there really is no incentive to seek outor
politically supportalternatives. To move away from auto-dependency,
we need to prevent that first purchase: If we want to move away
from auto-dependency, we need to build political support for the
kind of policies needed to make walking, bicycling, and public
transit more viable alternatives. And to build this political
support, we need to prevent that first purchase. We have to make it
at least possible, if not easy, to live without owning a car. The
taxi industry makes it possible to live in an auto-centric world,
without having to own your own car. If we can expand the number and
variety of trips that can be effectively served by taxis, the
dramatic difference in quality of life separating the car-dependent
from the car-free narrows. As the gulf narrows, more and more
people are able to make that leap away from auto-dependencyto live
rich and full lives on foot, by bike, on transit, and every so
often--in a taxi. The newly liberated expand the realm of what is
politically possible: more compact, dense development; the widening
of sidewalks; charging the right price for parking; policies which
finally put people first; closing off downtown streets every single
day of the week instead of once or twice a year! The possibilities
are endless.
Slide 4
Research Goal Goal : Expand the number and variety of trips
that can effectively be served by taxis in Los Angeles, with the
goal of supplementingnot replacingtrips on foot, bike, and transit
Taxicab Economics 101 [Cost of Taxi Service] = f (distance, time,
deadheading costs) In current system, customers pay a distance/time
based rate that factors in an average deadheading costthe cost of
returning from a destination without passengers Deadheading costs
are a SIGNIFICANT! A 4-mile trip from Westwood to Bel-Air costs
more for a taxi driver to serve than a 4-mile trip from Westwood to
Santa Monica, but they are priced exactly the same! Midterm
Research Question: What if we could identify zones in Los Angeles
where the deadheading costs are low? In other words, where the taxi
driver is very likely to be able to find a return fare? Final
Research Question: What would a network of taxi-friendly nodes in
Los Angeles look like? Where would the nodes be located, and how
much would it cost to travel between these nodes? Imagine getting
picked up in the center of Westwood Village and dropped off in the
middle of West Hollywoodall for $10!!!
Slide 5
Neighborhoods of the Westside 18 neighborhoods Generally
bounded by the Pacific Ocean to the West, Fairfax to the East, the
Santa Monica Mountains to the North, Manchester to the South
Average Median Household Income: $67,000 Intersected by two major
highways Map prepared by Earl Kaing Data Source: LA Times, 2000
Census, LA County CIO
Slide 6
For the Midterm % Multi-Unit Housing Score Median HH Income
Score Commercial Rent ScoreCommercial Density Score Commercial Taxi
Friendliness Residential Taxi Friendliness Maps prepared by Earl
Kaing Data Source: LA Times, 2000 Census, LA County CIO, LA County
Assessor
Slide 7
For the Final For the final, I will: 1.Use Map Algebra to
consolidate the maps of residential and commercial taxi
friendliness into a single map 2.Use Geocoding to place a taxi
stand at each area of taxi friendliness based on qualitative /
experiential knowledge of areas that are pedestrian friendly 3.Use
Service Area Analysis Determine how many people live within 15
minutes walk, and 15 minutes bicycling of each taxi stand 4.Create
an O-D Matrix to estimate the expected fare between each taxi stand
1. Map Algebra 2. Geocoding 3. Service Area 4. O-D Matrix
Slide 8
FINDINGS Source: D.L. Scrimger
Slide 9
Taxi Friendliness Components Commercial Taxi
FriendlinessResidential Taxi Friendliness [Residential Taxi
Friendliness] = [% Multi-Unit Housing Quintile] + [Median HH Income
Score*] *See appendix for calculation [Commercial Taxi
Friendliness] = [Commercial Density Quintile] + [Commercial Rent
Quintile] 1. Map Algebra 2. Geocoding 3. Service Area 4. O-D Matrix
Maps prepared by Earl Kaing Data Source: LA Times, 2000 Census, LA
County CIO, LA County Assessor
Slide 10
Aggregate Taxi Friendliness [Aggregate Taxi Friendliness] =
[Commercial Taxi Friendliness] + [Residential Taxi Friendliness] 1.
Map Algebra 2. Geocoding 3. Service Area 4. O-D Matrix Maps
prepared by Earl Kaing Data Source: LA Times, 2000 Census, LA
County CIO, LA County Assessor
Slide 11
Taxi Stand Locations 1. Map Algebra 2. Geocoding 3. Service
Area 4. O-D Matrix 1 2 3 4 5 6 Map prepared by Earl Kaing Data
Source: LA Times, 2000 Census, LA County CIO, LA County Assessor
Image Sources : Google Street View
Slide 12
Service Area (Walking) 1. Map Algebra 2. Geocoding 3. Service
Area 4. O-D Matrix Map prepared by Earl Kaing Data Source: LA
Times, 2000 Census, LA County CIO
Slide 13
Service Area (Bicycling) 1. Map Algebra 2. Geocoding 3. Service
Area 4. O-D Matrix Map prepared by Earl Kaing Data Source: LA
Times, 2000 Census, LA County CIO
Slide 14
A Comparison of Estimated Fares Traditional TaxiAwesome Taxi 1.
Map Algebra 2. Geocoding 3. Service Area4. O-D Matrix Map prepared
by Earl Kaing Data Source: LA Times, 2000 Census, LA County CIO
Fare Data: taxifarefinder.com
Slide 15
Potential Flat Fare Structure 1. Map Algebra 2. Geocoding 3.
Service Area4. O-D Matrix FromToFlat Fare SaMoVenice$5 SaMoWestwood
(Village)$10 SaMoLittle Osaka$10 SaMoWestwood (Campus)$10 SaMoWeHo
West$15 SaMoWeHo East$20 FromToFlat Fare VeniceSaMo$5 VeniceLittle
Osaka$10 VeniceWestwood (Village)$15 VeniceWestwood (Campus)$15
VeniceWeHo West$20 VeniceWeHo East$25 FromToFlat Fare WeHo EastWeHo
West$5 WeHo EastWestwood (Village)$15 WeHo EastLittle Osaka$15 WeHo
EastWestwood (Campus)$15 WeHo EastSaMo$20 WeHo EastVenice$25
FromToFlat Fare WeHo WestWeHo East$5 WeHo WestWestwood (Village)$10
WeHo WestLittle Osaka$10 WeHo WestWestwood (Campus)$10 WeHo
WestVenice$20 WeHo WestSaMo$20 FromToFlat Fare WestwoodWestwood
(Village)$5 WestwoodLittle Osaka$5 WestwoodWeHo West$10
WestwoodSaMo$10 WestwoodWeHo East$15 WestwoodVenice$15 FromToFlat
Fare Little OsakaWeHo East$5 Little OsakaWestwood (Village)$5
Little OsakaWestwood (Campus)$10 Little OsakaVenice$10 Little
OsakaSaMo$10 Little OsakaWeHo West$15
Slide 16
Questions? Source: D.L. Scrimger
Slide 17
APPENDIX Requirements Checklist
Slide 18
RequirementHow Met? 8 Layouts: Does presentation include a
minimum of 8 layouts? 1.Communities of the Westside (Slide 4) 2.For
the Midterm (Slide 5) 3.Taxi Friendliness Components (Slide 7)
4.Aggregate Taxi Friendliness (Slide 8) 5.Taxi Stand Locations
(Slide 9) 6.Service Area Walking (Slide 10) 7.Service Area Biking
(Slide 11) 8.A Comparison of Estimated Fares (Slide 12) 7 Layers:
Does at least one layout include seven (7) or more layers? Service
Area Biking (Slide 11) 1.California Shoreline 2.Communities of the
Westside 3.Major Highways 4.Tiger Roads 5.Taxi Stand Locations
6.Service Area Layer (5 min) 7.Service Area Layer (10 min)
8.Service Area Layer (15 min) Modeling: Does your presentation use
a model to automate data manipulation? Is this model diagram
included as a jpg at the end of the presentation or following the
layout it was used in? To create Aggregate Taxi Friendliness (Slide
8), I used a model to 1) convert the four components of taxi
friendliness (2 residential and 2 commercial) into separate
rasters, and then 2) reclassify each of these rasters into an
indexed score from 1-5. A screenshot of this model can be found in
the appendix.
Slide 19
Requirements Checklist RequirementHow Met? Metadata: Does your
project include at least one metadata sheet for at least one of
your original geographic layers or elements? Is the screenshot of
this metadata sheet included at the end of the presentation? I
created a metadata sheet for the Communities of the Westside shape
file that I created. The screenshot of the metadata sheet can be
found in the appendix. Measurement/Analysis: Does your project
include a measurement analysis that integrates some measure of
distance (buffer, concentric zones, elements displayed a certain
distance from a central feature, nearest neighbor, or display
lines/circles a given distance from a feature, etc)? I used ArcGIS
Network Analyst to calculate walking and biking service areas for
each taxi stand node based on 1) tiger roads distance; 2) average
walking speed; and 3) average biking speed. The service areas
illustrate temporal distance from each taxi stand. Original Data:
Does your project include an original map layer created using data
from outside sources? I used georeferencing and feature editing to
create the Communities of the Westside layer seen in Slide 4. In
the midterm, I used a different shapefile downloaded from the LA
County GIS portal. For the final, I took the shapefile I used in
the midterm, and edited the features to match a georeferenced
screenshot (JPEG) of Mapping LAs Communities of the Westside page.
Descriptive Map: Does your powerpoint include a descriptive map
that provides a general overview of your study area? The
Communities of the Westside (Slide 4) provides a general overview
of the study area.
Slide 20
Requirements Checklist RequirementHow Met? Six Additional
Skills: Does your project utilize at least six other skills, one of
which is drawn from the following? Extracting information from a
buffer Charts, graphs, or images Hotspot analysis Network analysis
Spatial analysis Elevation 3-d modeling Google Mash-Up 1.Charts,
Graphs & Images: To help give the audience a better feel for
the built environment around each taxi stand location I integrated
images from Google street view for each location into the layout of
Taxi Stand Locations (Slide 9). 2.Network Analysis (Service Area):
I used network analyst to calculate the 5, 10, and 15 minute
service areas around each taxi stand, for both the walking and
biking modalities. 3.Network Analysis 2 (O-D Matrix): I used
network analyst to generate a matrix of network travel costs (in
minutes) from each taxi stand location to all other taxi stands. I
then used this matrix to estimate the dollar cost of service, based
on the assumption that under this new system, service costs can be
cut in half. 4.Hotspot Analysis: I used hotspot analysis to create
the Aggregate Taxi Friendliness layout (Slide 8) by calculating the
intersection of the two commercial and two residential taxi
friendliness factors.
Slide 21
Requirements Checklist RequirementHow Met? Six Additional
Skills (cont.): Does your project utilize at least six other
skills, one of which is drawn from the following? Extracting
information from a buffer Charts, graphs, or images Hotspot
analysis Network analysis Spatial analysis Elevation 3-d modeling
Google Mash-Up 5.Extracting Information From a Buffer: To calculate
the total population within 15 minutes biking, and within 15
minutes walking of each taxi stand, I: a.dissolved the 5, 10, and
15 minute service areas for each modality into a single feature
(the buffer) b.performed a spatial join between the buffer and the
underlying census tracts (to which population counts had been
joined) to sum up the population of all census tracts intersecting
the buffer c.estimated the population within the buffer as the
proportion of the area of the buffer to the total area of all
intersecting census tracts 6.Inset Map: Used in Slide 4
(Communities of the Westside) to show the Westside Region in the
context of Los Angeles County. Also used in Slide 8 (Aggregate Taxi
Friendliness) to help transition from a higher level of zoom to a
lower level of zoom. 7.Line Graduated Symbol: Used in Slide 12 (A
Comparison of Estimated Fares) to distinguish between low cost
trips ($0- $15), in green; medium cost trips ($15-30), in yellow;
and high cost trips ($30-$50), in red.
Slide 22
Requirements Checklist RequirementHow Met? Six Additional
Skills (cont.): Does your project utilize at least six other
skills, one of which is drawn from the following? Extracting
information from a buffer Charts, graphs, or images Hotspot
analysis Network analysis Spatial analysis Elevation 3-d modeling
Google Mash-Up 8.Creating Indices: created an aggregate taxi
friendliness indicator by combining the residential and commercial
taxi friendliness scores from the midterm, without any weights. The
residential taxi friendliness = f(% multi-unit housing, median HH
income). The commercial taxi friendliness = f(commercial parcel
density, commercial rent ($/sqft) ). 9.Geocoding: to identify the
taxi stand locations seen in Slide 9, I started at areas with high
taxi friendliness scores, and then used my experiential knowledge
and Google Street View to identify specific cross streets which
would be ideal for a taxi stand. I then geocoded these
intersections, using an address locator that I created based on the
tiger roads shape file.
Slide 23
APPENDIX Step-by-Step Methods
Slide 24
Communities of the Westside 1.Took a JPEG of Westside Region
from Mapping LA website 2.Georeferenced JPEG to give it coordinates
3.Used georeferenced as basis to create a new shapefile by editing
the unofficial LA County communities shapefile to match the Mapping
LA JPEG 4.Included the neighborhood of West Hollywood in my
definition of the Westside, even though its not included by the
Mapping LA project 5.Used new shape file to determine which census
tracts to consider in analysis. Any census tracts which intersected
a Westside neighborhood was included. All other tracts were clipped
away.
Slide 25
Map Algebra Combine the separate residential and commercial
taxi friendliness maps into a single taxi friendliness map. 1.Used
a model to convert residential shape file and commercial shape file
into four separate rasters 2.Used model to reclass each rasters.
All were reclassed based on quintiles, with the exception of
income, which I reclassed based on standard deviations from the
average median income on The Westside 3.Used Map Algebra >
Raster Calculator to add the two residential rasters to get a
residential index. Repeated process with the commercial rasters to
get a commercial index. 4.Added the two rasters together to create
an aggregate taxi friendliness index 1. Map Algebra 2. Geocoding 3.
Service Area 4. O-D Matrix
Slide 26
Model 1. Map Algebra 2. Geocoding 3. Service Area 4. O-D
Matrix
Slide 27
Metadata 1. Map Algebra 2. Geocoding 3. Service Area 4. O-D
Matrix
Slide 28
Taxi Stand Locations For each neighborhood, determine the best
location to place a taxi stand. 1.Using the raster of taxi friendly
census tract, I classified out of a total possible friendliness
score of 20, those in the 90-100%, 80-90%, 70-80%, and 60-80%
range. 2.Based on the raster, I identified unique clusters of
90-100% taxi friendliness within each neighborhood. Most
neighborhoods had one distinct cluster, but some, like Santa
Monica, had two. 3.I used Google maps, along with qualitative and
experiential knowledge to identify specific cross streets for the
taxi stands. I was looking for locations that were human-scale and
pedestrian friendly. 4.Based on this analysis, I identified the
following areas: 1.Santa Monica: SMB & 4 th 2.Sawtelle:
Sawtelle and Olympic 3.Venice: Abbot Kinney & Westminster
4.West Hollywood: San Vicente & SMB; Martel & SMB
5.Westwood: Weyburn and Broxton; Westwood and Strathmore 5.I used
the Tigerroads shape file for Los Angeles, clipped to the Westside,
and created an address locator based on it. The roads have dual
ranges. 6.I then used this address locator to geocode the locations
I had identified as most appropriate for a taxi stand. 7.For this
layout, I included pictures of the intersection where the stand
will be located, for visual reference. 1. Map Algebra 2. Geocoding
3. Service Area 4. O-D Matrix
Slide 29
Service Area Create a service area to see who is 5, 10, 15
minutes away from the stand on foot, and by bike. 1.I calculated a
segment length for each road feature in the tiger roads shape file.
2.I then calculated impedence = [length] / [speed] for driving,
biking, and walking, where I assumed: average driving speed across
the entire network of 25 mph (DMV speed limit in all
business/residential districts unless otherwise posted) average
walking speed of 3 mph average biking speed of 15 mph 3.Next, I
created a network dataset using the updated tiger roads file 4.I
then used spatial analyst to create a service area analysis layer
for walking and biking. What area is within 5, 10, and 15 min
walking or biking of the taxi stand? How many people live within 15
minutes walk or bike of each taxi stand? 1.Dissolve the 5, 10, and
15 minute service areas into a single buffer layer. 2.Join the
buffer layer with the census data layer containing information
about population per census tract 3.Extract information based on
spatial location, to sum up the population of all census tracts
which intersect the buffer 4.Estimate the population within the
buffer only using a factor = [area of buffer] / [total area of all
census tracts which intersect buffer] 5.Repeat this for both the
walking and the biking service area. 6.Represent taxi stand access
with graduated symbols based on population served. 1. Map Algebra
2. Geocoding 3. Service Area 4. O-D Matrix
Slide 30
O-D Matrix Estimate cost of service between each node 1.Use
network analyst to calculate an O-D matrix for the network. 2.If we
assume current prices are twice as high as they need to be because
of deadheading, then the new rate per unit time/distance for this
new proposed schematic can be divided by two 3.I use taxi fare
calculator available online to see what the rate would be under
current price regime. It turns out taxi trips average about $1.6
per minute. Thus the new price would be $0.8 per minute.
4.Calculate the new cost, using the driving time (minutes) between
each node from the O-D matrix 1. Map Algebra 2. Geocoding 3.
Service Area4. O-D Matrix