taxis are our friends mapping the “taxi-friendliness” of neighborhoods in the westside of los...

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

INTRODUCTION Source: D.L. Scrimger

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.

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!!!

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

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

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

FINDINGS Source: D.L. Scrimger

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

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

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

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

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

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

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

Questions? Source: D.L. Scrimger

APPENDIX Requirements Checklist

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.

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.

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.

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.

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.

APPENDIX Step-by-Step Methods

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.

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

Model 1. Map Algebra 2. Geocoding 3. Service Area 4. O-D Matrix

Metadata 1. Map Algebra 2. Geocoding 3. Service Area 4. O-D Matrix

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

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

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

taxis are our friends mapping the “taxi-friendliness” of neighborhoods in the westside of los...

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deadheading costs

taxi driver

network of taxi

taxi industry

cost of taxi service

earl kaing slide

marginal costs

research goal goal