From bd1f0e2cedab81566d323f08a0d3f0e42b1fd496 Mon Sep 17 00:00:00 2001 From: ridhi96 Date: Tue, 28 Nov 2023 06:42:50 +0000 Subject: [PATCH] updated analysis --- dbscan.ipynb | 988 ++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 779 insertions(+), 209 deletions(-) diff --git a/dbscan.ipynb b/dbscan.ipynb index cca5d4e..c9ece57 100644 --- a/dbscan.ipynb +++ b/dbscan.ipynb @@ -7,7 +7,7 @@ "source": [ "# CLUSTERING FOR INFERENCE\n", "\n", - "This notebook explores unsupervised machine learning to create clusters of cab rides based on geolocation (latitude and longitude). This is done to identify ride trends in Hyde Park before and after UChicago implemented the Lyft Ride program at a cluster level.`" + "This notebook explores unsupervised machine learning to understand the impact of University of Chicago Lyft Ride Program by creating clusters of cab rides based on geolocation (latitude and longitude). This is done to identify ride trends in Hyde Park before and after UChicago implemented the Lyft Ride program. The goal is to figure out locations around which customers tend to book rides most often and see if the Lyft program impacted this in any way." ] }, { @@ -19,17 +19,20 @@ { "data": { "text/plain": [ - "[('spark.stage.maxConsecutiveAttempts', '10'),\n", + "[('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n", + " 'http://hub-msca-bdp-dphub-students-test-ridhi-m:8088/proxy/application_1701140970875_0003'),\n", + " ('spark.stage.maxConsecutiveAttempts', '10'),\n", " ('spark.dynamicAllocation.minExecutors', '1'),\n", " ('spark.eventLog.enabled', 'true'),\n", " ('spark.submit.pyFiles',\n", " '/root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,/root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,/root/.ivy2/jars/com.typesafe_config-1.4.2.jar,/root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,/root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,/root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,/root/.ivy2/jars/com.navigamez_greex-1.0.jar,/root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,/root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,/root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,/root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,/root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,/root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,/root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,/root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,/root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,/root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,/root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,/root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,/root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,/root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,/root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,/root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,/root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,/root/.ivy2/jars/com.google.api_gax-2.20.1.jar,/root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,/root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,/root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,/root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,/root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,/root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,/root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,/root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,/root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,/root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,/root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,/root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,/root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,/root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,/root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,/root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,/root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,/root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,/root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,/root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,/root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n", + " ('spark.app.startTime', '1701153586673'),\n", " ('spark.dataproc.sql.joinConditionReorder.enabled', 'true'),\n", " ('spark.sql.autoBroadcastJoinThreshold', '191m'),\n", - " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n", - " 'http://hub-msca-bdp-dphub-students-test-ridhi-m:8088/proxy/application_1700930017790_0001'),\n", " ('spark.kryoserializer.buffer.max', '2000M'),\n", " ('spark.serializer', 'org.apache.spark.serializer.KryoSerializer'),\n", + " ('spark.driver.appUIAddress',\n", + " 'http://hub-msca-bdp-dphub-students-test-ridhi-m.c.msca-bdp-student-ap.internal:39219'),\n", " ('spark.dataproc.sql.local.rank.pushdown.enabled', 'true'),\n", " ('spark.driver.maxResultSize', '0'),\n", " ('spark.yarn.unmanagedAM.enabled', 'true'),\n", @@ -37,7 +40,6 @@ " 'org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter'),\n", " ('spark.metrics.namespace',\n", " 'app_name:${spark.app.name}.app_id:${spark.app.id}'),\n", - " ('spark.app.id', 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'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n", " ('spark.sql.cbo.enabled', 'true'),\n", - " ('spark.driver.port', '36689'),\n", " ('spark.executorEnv.PYTHONPATH',\n", " 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" ('spark.yarn.dist.jars',\n", @@ -83,13 +80,15 @@ " 'com.google.cloud.spark.performance.DataprocMetricsListener'),\n", " ('spark.driver.memory', '8g'),\n", " ('spark.serializer.objectStreamReset', '100'),\n", - " ('spark.ui.proxyBase', '/proxy/application_1700930017790_0001'),\n", + " ('spark.ui.proxyBase', '/proxy/application_1701140970875_0003'),\n", " ('spark.executor.memory', '8g'),\n", - " ('spark.app.startTime', '1700930202840'),\n", " ('spark.submit.deployMode', 'client'),\n", " ('spark.executor.cores', '8'),\n", " ('spark.sql.cbo.joinReorder.enabled', 'true'),\n", " ('spark.shuffle.service.enabled', 'true'),\n", + " ('spark.app.id', 'application_1701140970875_0003'),\n", + " ('spark.eventLog.dir',\n", + " 'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/23e5ee58-dae4-4eaf-9859-8e28ccecf1f8/spark-job-history'),\n", " ('spark.scheduler.mode', 'FAIR'),\n", " ('spark.sql.adaptive.enabled', 'true'),\n", " ('spark.yarn.jars', 'local:/usr/lib/spark/jars/*'),\n", @@ -98,6 +97,7 @@ " ('spark.ui.port', '0'),\n", " ('spark.rpc.message.maxSize', '512'),\n", " ('spark.rdd.compress', 'True'),\n", + " ('spark.driver.port', '44505'),\n", " ('spark.task.maxFailures', '10'),\n", " ('spark.yarn.isPython', 'true'),\n", " ('spark.dynamicAllocation.enabled', 'true'),\n", @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "id": "b6c5fdcd-e1af-4e2a-b177-7acc4e3dbec6", "metadata": {}, "outputs": [ @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "8d149ef9-cefd-4c23-82e0-8f23618102e8", "metadata": {}, "outputs": [ @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "acae673f-ccc0-4b14-828e-bbaac88d357b", "metadata": {}, "outputs": [ @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "33165f38-fe04-4100-b615-8419b2d12578", "metadata": {}, "outputs": [], @@ -238,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "1df809f1-d2ba-444d-b33e-82cb0ff8feb0", "metadata": {}, "outputs": [ @@ -246,14 +246,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Partitions: 16\n" + "Partitions: 45\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Stage 17:======================================> (11 + 5) / 16]\r" + "[Stage 17:======================================================> (44 + 1) / 45]\r" ] }, { @@ -263,22 +263,51 @@ "+-----------+-----+\n", "|partitionId|count|\n", "+-----------+-----+\n", - "| 7| 3242|\n", - "| 6| 3731|\n", - "| 5| 3982|\n", - "| 4| 4298|\n", - "| 3| 4702|\n", - "| 2| 7344|\n", - "| 1| 9349|\n", - "| 0|10993|\n", - "| 15|12512|\n", - "| 14|16357|\n", - "| 13|18177|\n", - "| 12|20071|\n", - "| 11|20717|\n", - "| 10|33168|\n", - "| 8|38440|\n", - "| 9|39548|\n", + "| 22| 557|\n", + "| 21| 1205|\n", + "| 20| 1267|\n", + "| 19| 1290|\n", + "| 18| 1320|\n", + "| 17| 1334|\n", + "| 16| 1381|\n", + "| 15| 1406|\n", + "| 14| 1438|\n", + "| 13| 1489|\n", + "| 12| 1518|\n", + "| 11| 1575|\n", + "| 10| 1615|\n", + "| 9| 1689|\n", + "| 8| 1763|\n", + "| 7| 2383|\n", + "| 6| 3036|\n", + "| 5| 3120|\n", + "| 4| 3192|\n", + "| 3| 3446|\n", + "| 2| 3761|\n", + "| 1| 3818|\n", + "| 0| 4038|\n", + "| 44| 6278|\n", + "| 43| 8539|\n", + "| 42| 8623|\n", + "| 41| 8706|\n", + "| 40| 8774|\n", + "| 39| 8841|\n", + "| 38| 8979|\n", + "| 37|10838|\n", + "| 36|11432|\n", + "| 35|11593|\n", + "| 34|11641|\n", + "| 33|11724|\n", + "| 32|11884|\n", + "| 31|14229|\n", + "| 30|18323|\n", + "| 29|18405|\n", + "| 28|18474|\n", + "| 27|18623|\n", + "| 26|18692|\n", + "| 25|18747|\n", + "| 24|18840|\n", + "| 23|19089|\n", "+-----------+-----+\n", "\n" ] @@ -364,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "410f371d-f9c7-48d5-a795-f1f8ef52eeb0", "metadata": {}, "outputs": [], @@ -383,32 +412,30 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "id": "b419e72b-6458-4dd5-942f-0bde079c11e5", "metadata": {}, "outputs": [], "source": [ + "from pyspark.sql.functions import col, radians, acos, sin, cos, lit, unix_timestamp\n", + "from pyspark.ml.feature import VectorAssembler, StandardScaler\n", + "from pyspark.ml.clustering import KMeans\n", + "from pyspark.ml import Pipeline\n", + "from pyspark.ml.evaluation import ClusteringEvaluator\n", + "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n", + "import geopandas as gpd\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "from sklearn.cluster import DBSCAN\n", "from sklearn import metrics\n", - "import time\n", - "from pyspark.sql.functions import col, radians, acos, sin, cos, lit, unix_timestamp\n", - "import time\n", - "from pyspark.ml.feature import VectorAssembler\n", - "import geopandas as gpd\n", - "from pyspark.ml.feature import VectorAssembler\n", - "from pyspark.ml.clustering import KMeans\n", - "from pyspark.ml import Pipeline\n", - "from pyspark.ml.feature import StandardScaler\n", - "from pyspark.ml.evaluation import ClusteringEvaluator" + "import time" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "87108e84-9d70-4174-ba1b-b60ce5af2ba0", "metadata": {}, "outputs": [], @@ -442,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "014acbb8-d702-40b0-aa2b-04dd4b50d892", "metadata": {}, "outputs": [], @@ -450,31 +477,23 @@ "# creating column to measure trip duration\n", "\n", "before = calculate_trip_duration(before)\n", - "after = calculate_trip_duration(after)\n", - "df_2018 = calculate_trip_duration(df_2018)\n", - "df_2019 = calculate_trip_duration(df_2019)\n", - "df_2021 = calculate_trip_duration(df_2021)\n", - "df_2022 = calculate_trip_duration(df_2022)\n", - "df_2023 = calculate_trip_duration(df_2023)" + "after = calculate_trip_duration(after)" ] }, { - "cell_type": "code", - "execution_count": 109, - "id": "4e581e1d-d710-4c5b-87e2-39a5b82c91e8", + "cell_type": "markdown", + "id": "035a44cd-33b6-4f17-b728-9138787699f0", "metadata": {}, - "outputs": [], "source": [ - "before = calculate_trip_duration(before)\n", - "after = calculate_trip_duration(after)" + "# Modeling" ] }, { "cell_type": "markdown", - "id": "035a44cd-33b6-4f17-b728-9138787699f0", + "id": "33892586-050a-4f21-a275-99aa1adb543c", "metadata": {}, "source": [ - "# Modeling" + "A machine learning model using K-Means clustering is created that will group ride pickup and dropoff locations based on their similarity in terms of latitude and longitude." ] }, { @@ -510,18 +529,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "id": "325dfded-9703-4997-828c-c86d4a493e57", "metadata": {}, "outputs": [], "source": [ - "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n", - "from pyspark.ml.evaluation import ClusteringEvaluator\n", - "\n", - "\n", "paramGrid = ParamGridBuilder() \\\n", - " .addGrid(kmeans_pickup.k, [3, 5, 7, 8, 10, 12, 13, 15]) \\\n", - " .addGrid(kmeans_dropoff.k, [3, 5, 7, 8, 10, 12, 13, 15]) \\\n", + " .addGrid(kmeans_pickup.k, [3, 7, 10, 13, 15]) \\\n", + " .addGrid(kmeans_dropoff.k, [3, 7, 10, 13, 15]) \\\n", " .build()\n", "\n", "# Set up the evaluator\n", @@ -547,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "id": "f5b59f80-c0d6-4bac-9481-644a39a95f16", "metadata": {}, "outputs": [], @@ -571,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "id": "dfadfe31-66fd-4df6-b574-d3c64e1798c9", "metadata": {}, "outputs": [], @@ -592,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "85a97f41-4929-4d2d-8bc4-a6a2dc2637bd", "metadata": {}, "outputs": [ @@ -600,6 +615,8 @@ "name": "stderr", "output_type": "stream", "text": [ + "23/11/28 03:47:14 WARN com.github.fommil.netlib.BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS\n", + "23/11/28 03:47:15 WARN com.github.fommil.netlib.BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS\n", " \r" ] } @@ -613,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "id": "9d801fa5-ff78-4d77-aa37-accded102fea", "metadata": {}, "outputs": [ @@ -623,8 +640,8 @@ "text": [ "root\n", " |-- ID: string (nullable = true)\n", - " |-- start_timestamp: timestamp (nullable = true)\n", - " |-- end_timestamp: timestamp (nullable = true)\n", + " |-- start_timestamp: long (nullable = true)\n", + " |-- end_timestamp: long (nullable = true)\n", " |-- seconds: integer (nullable = true)\n", " |-- miles: double (nullable = true)\n", " |-- pickup_tract: long (nullable = true)\n", @@ -642,43 +659,73 @@ " |-- day_of_month: integer (nullable = true)\n", " |-- hour: integer (nullable = true)\n", " |-- day: integer (nullable = true)\n", + " |-- trip_duration: double (nullable = true)\n", " |-- pickup_features: vector (nullable = true)\n", " |-- pickup_cluster: integer (nullable = false)\n", " |-- dropoff_features: vector (nullable = true)\n", " |-- dropoff_cluster: integer (nullable = false)\n", - "\n", - "+--------------+\n", - "|pickup_cluster|\n", - "+--------------+\n", - "| 10|\n", - "| 14|\n", - "| 1|\n", - "| 10|\n", - "| 10|\n", - "| 3|\n", - "| 10|\n", - "| 9|\n", - "| 10|\n", - "| 11|\n", - "| 11|\n", - "| 6|\n", - "| 11|\n", - "| 14|\n", - "| 8|\n", - "| 9|\n", - "| 6|\n", - "| 3|\n", - "| 10|\n", - "| 6|\n", - "+--------------+\n", - "only showing top 20 rows\n", "\n" ] } ], "source": [ - "before.printSchema()\n", - "before.select('pickup_cluster').show()" + "before.printSchema()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e36fae2e-04af-4adb-b585-d47ae8817e7f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "before.select('pickup_cluster').distinct().count()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "104ee3f3-6778-4702-8166-d5304fa4d85f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "after.select('pickup_cluster').distinct().count()" ] }, { @@ -772,9 +819,25 @@ "df_2018.printSchema()" ] }, + { + "cell_type": "markdown", + "id": "a09923a2-4df1-4ca5-81fe-309053996948", + "metadata": {}, + "source": [ + "## Analaysis" + ] + }, + { + "cell_type": "markdown", + "id": "2b7bd593-5948-4d37-9d22-0f572c769f26", + "metadata": {}, + "source": [ + "The before and after datasets are converted to pandas dataframes to visualize clusters for analysis." + ] + }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "id": "94e421c0-78b4-4780-aaae-e0deacc8c21d", "metadata": {}, "outputs": [ @@ -793,15 +856,23 @@ " 'dropoff_cluster').toPandas()" ] }, + { + "cell_type": "markdown", + "id": "d05b7640-1b67-49c4-b726-ff3de5ebc3fd", + "metadata": {}, + "source": [ + "## 1) Before and After program pickup and dropoff cluster visualization" + ] + }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 30, "id": "79aa4cb5-1eb3-483d-8c51-cc170ac15b5e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -839,9 +910,29 @@ "plt.show()\n" ] }, + { + "cell_type": "markdown", + "id": "98fbc997-5419-426a-bdf8-ed38aea3d52b", + "metadata": {}, + "source": [ + "The plots in the first row shows the pickup and dropoff location clusters in the Hyde Park program area before the Lyft program was implemented. The plots in the second row shows the pickup and dropoff location clusters in the Hyde Park program area after the Lyft program was implemented.\n", + "\n", + "It appears that the locations that people take a ride to and from in Hyde Park have not been affected that much by the Lyft Ride Program. This makes sense because in the university area, most of the population is students and they tend to stay near the unversity in certain preferable locations." + ] + }, + { + "cell_type": "markdown", + "id": "48c8d1a9-3af7-422f-b6e0-6385dd04de13", + "metadata": {}, + "source": [ + "## 2) Centermost cluster points analysis\n", + "\n", + "The next part of the analysis, the centermost points of the clusters are evaluated. Based on the latitudes and longitudes in a cluster, the centermost latitude and longitude location is calculated. This is a way to reduce the location information present in a cluster down to one point which can be used for a generalized analysis of the clusters." + ] + }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 33, "id": "dbde458f-1123-4196-ae9f-4ff9eb508170", "metadata": {}, "outputs": [], @@ -887,13 +978,13 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 34, "id": "cd6656bf-8093-4428-8402-48e08f1258fc", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -966,15 +1057,23 @@ "plt.show()\n" ] }, + { + "cell_type": "markdown", + "id": "edf31b89-8417-470f-9675-246fea80b9b7", + "metadata": {}, + "source": [ + "The pickup centermost location points seem to show some variation before and after the program. This implies that the pickup clusters identified by the K-Means algorithms are not identical which indicates some shift in the behavior of customers in terms of where they take rides from in Hyde Park before and after the program." + ] + }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 35, "id": "960d215b-b04c-448e-a779-5670abd47192", "metadata": {}, "outputs": [ { "data": { - "image/png": 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nTh19/vnnmjhx4ilf77P9e3I6PPl+KdWoUaMyP+NS1flzOJkePXqoR48ecjgc+uGHH/S3v/1NI0eOVHR0tIYMGeLx8QEAqAyCGgBAlVi/fr0kuUKVq6++WrNnz5bD4VCXLl1O+3h9+vTRpEmT9OGHH+qJJ55wtX/66afKyclRnz59TnmMq6++WgsWLFDjxo3LfLg/ldatW0squcyrX79+rvbRo0fL4XBozJgxCg0N1WuvvVbhcbZs2aLVq1frhhtuKHdr5wkTJujzzz93CxZOVLpd9YmzBrp166bw8HBt3ry5Src2l3TSD9tnYvz48dq8ebOeffZZBQQEVNi3efPmio+P10cffaQ//vGPrqAgJydHn376qWsnqOPNmjVLnTp1ct3/5JNPVFxcrF69ekkq2VJdkj788EO3gC85OVlbtmw5rW2jT8XX11ejR4/W6NGj9dJLL5XZnluSMjIytG3bNnXr1u2s/56Uxxvvl8qorp/Dyc7/eHa7XV26dFGLFi00a9YsrV27lqAGAFBjENQAAE7bxo0bXdtbHzp0SHPnzlVSUpKuv/5618yLIUOGaNasWbrqqqv0+OOPq3PnzvL19dWePXu0dOlSXXvttbr++utPOka/fv10xRVXaPTo0crKylK3bt30008/6YUXXlCHDh10xx13nLLOF198UUlJSeratasee+wxNW/eXPn5+dq5c6cWLFigf/zjH2W+2S/VunVrPfTQQ5o2bZqysrJ05513KiwsTJs2bdLbb7+tevXqae/evRo7dqz++te/nrSG0tk0Tz31lDp37lzm8WPHjunrr7/Whx9+eNLLL0pDo+nTpyskJEQBAQFKTExURESE/va3v2nYsGE6fPiwbrzxRkVFRenAgQP68ccfdeDAAU2bNu2Ur1NVOXr0qL777jtJJcHK1q1bNXv2bP33v//VzTff7NoyvSI2m02TJk3SbbfdpquvvloPPPCACgoK9Nprr+no0aP605/+VOY5c+fOlY+Pj/r166dNmzZp7NixateunW6++WZJJeHP/fffr7/97W+y2WwaMGCAdu7cqbFjx6p+/fpuQWBVePLJJ7Vlyxa98MIL+v777zV06FDVr19fmZmZWrFihaZPn67x48erW7duZ/33pDw19f1SXT+HNm3aSJL+8pe/aNiwYfL19VXz5s01a9YsffPNNxo4cKAaNGig/Px819bmffv2rZKxAQCoEt5dyxgAcC4pb9ensLAw0759e/PGG2+4bZltjDFFRUXmz3/+s2nXrp0JCAgwtWrVMi1atDAPPPCA2bZtm6vfyXbUycvLM6NHjzYJCQnG19fXxMbGmoceeqjMVroV7Th04MAB89hjj5nExETj6+tr6tSpYzp16mTGjBljsrOzKzxfp9NpZsyYYTp37mwCAwNNQECAadeunfnTn/5kcnJyzOuvv17urkOlCgsLTVRUlGnfvv1JxyguLjb16tUzbdq0McaUv+uTMcZMnjzZJCYmGrvdbiSZmTNnuh5bvny5GThwoKlTp47x9fU18fHxZuDAgW5bJZ9sN6OqkpCQ4HpPWJZlatWqZZo3b27uuOMOs2jRojL9S3d9eu2118o93rx580yXLl1MQECACQ4ONn369DHffvutW5/Sc1qzZo0ZNGiQqVWrlgkJCTG33nqr2b9/v1tfh8NhXn31VdOsWTPj6+trIiMjze233252795d7jHPdNen433++edm4MCBpm7dusbHx8fUrl3b9O7d2/zjH/8wBQUFrn6V/Xtysvd5z549Tc+ePd3aqvv9crLtuU9U2Z/DyXZ9GjFiRJljJiQkmGHDhrm1PfPMMyYuLs7YbDbXDlirV682119/vUlISDD+/v4mIiLC9OzZ08yfP79S5wgAQHWxjDnLbRwAAAC8YNy4cRo/frwOHDhQLeubAAAAVAe25wYAAAAAAKghCGoAAAAAAABqCC59AgAAAAAAqCGYUQMAAAAAAFBDENQAAAAAAADUEAQ1AAAAAAAANYSPtws4nzmdTu3bt08hISGyLMvb5QAAAAAAznPGGB07dkxxcXGy2ZibcS4iqPGgffv2qX79+t4uAwAAAABwgdm9e7fq1avn7TJwBghqPCgkJERSyV+Q0NBQL1cDAAAAADjfZWVlqX79+q7Pozj3ENR4UOnlTqGhoQQ1AAAAAIBqw/Ib5y4uWAMAAAAAAKghCGoAAAAAAABqCIIaAAAAAACAGoI1agAAAAAAuIAYY1RcXCyHw+HtUi4YdrtdPj4+lVo7iKAGAAAAAIALRGFhodLS0pSbm+vtUi44QUFBio2NlZ+fX4X9akxQM3HiRD377LN6/PHHNXnyZEnS3Llz9c9//lNr1qzRoUOHtG7dOrVv3/6Ux5o8ebKmTZum1NRURUZG6sYbb9TEiRMVEBAgSVqxYoVee+01rVmzRmlpafrss8903XXXuR3DGKPx48dr+vTpOnLkiLp06aK///3vuuiii6r4zAEAAAAA8Dyn06mUlBTZ7XbFxcXJz8+P3aGqgTFGhYWFOnDggFJSUtS0aVPZbCdfiaZGBDXJycmaPn262rZt69aek5Ojbt266aabbtJ9991XqWPNmjVLTz/9tN555x117dpVv/zyi+666y5J0ptvvuk6brt27TR8+HDdcMMN5R5n0qRJeuONN/Tuu++qWbNmmjBhgvr166etW7eyHz0AAAAA4JxTWFgop9Op+vXrKygoyNvlXFACAwPl6+urXbt2qbCw0DWRpDxeD2qys7N122236a233tKECRPcHrvjjjskSTt37qz08VavXq1u3bpp6NChkqSGDRvq1ltv1ffff+/qM2DAAA0YMOCkxzDGaPLkyRozZowGDx4sSXrvvfcUHR2tjz76SA888ECl6wEAAAAAoCapaDYHPKeyr7vXfzojRozQwIED1bdv3yo5Xvfu3bVmzRpXMLNjxw4tWLBAAwcOrPQxUlJSlJ6erv79+7va/P391bNnT61ateqkzysoKFBWVpbbDQAAAACAc5pxerY/3Hh1Rs3s2bO1du1aJScnV9kxhwwZogMHDqh79+6ulawfeughPf3005U+Rnp6uiQpOjrarT06Olq7du066fMmTpyo8ePHn1nhAAAAAADUNI4iqbhQ8vGX7JWIEIoLS57jGyDZ7J6v7zzktRk1u3fv1uOPP64PP/ywwmuzTteyZcv08ssva+rUqVq7dq3mzp2rL7/8Ui+99NJpH+vERZWMMRUutPTMM88oMzPTddu9e/dpjwkAAAAAQI3gdEhFhZKRVFwgOYor7l9cKBUXlfQvyq+RM2vGjRun6OhoWZalefPmebuccnktqFmzZo0yMjLUqVMn+fj4yMfHR8uXL9df//pX+fj4nPF+7mPHjtUdd9yhe++9V23atNH111+vV155RRMnTpTTWbk3SUxMjKTfZ9aUysjIKDPL5nj+/v4KDQ11uwEAAAAAcE6ybL/PojlVWFMa0pSy+5U8v4rcddddsizLdYuIiNCVV16pn376qdLH2LJli8aPH69//vOfSktLq3DtWm/yWlDTp08fbdiwQevXr3fdLr74Yt12221av3697PYzmyKVm5tbZoEeu90uY4yMMZU6RmJiomJiYpSUlORqKyws1PLly9W1a9czqgsAAAAAgHOKZUk+fqcOa04MaXz8JB/fKi/nyiuvVFpamtLS0vT111/Lx8dHV199daWf/+uvv0qSrr32WsXExMjf3/+M6igqKjp1p7PgtaAmJCRErVu3drsFBwcrIiJCrVu3liQdPnxY69ev1+bNmyVJW7du1fr1691mutx555165plnXPcHDRqkadOmafbs2UpJSVFSUpLGjh2ra665xhX+ZGdnu8IhqWTx4PXr1ys1NVVSySVPI0eO1CuvvKLPPvtMGzdu1F133aWgoCDXblIAAAAAAJz3ThXWVFNII5VcxRITE6OYmBi1b99eo0eP1u7du3XgwAFJ0t69e3XLLbeodu3aioiI0LXXXuvaRXrcuHEaNGiQpJLdl0qXNXE6nXrxxRdVr149+fv7q3379lq4cKFrzJ07d8qyLH3yySfq1auXAgIC9OGHH0qSZs6cqZYtWyogIEAtWrTQ1KlTq+Q8vb49d0Xmz5+v4cOHu+4PGTJEkvTCCy9o3LhxkqTU1FS3GTTPPfecLMvSc889p71796pu3boaNGiQXn75ZVefH374Qb1793bdHzVqlCRp2LBhevfddyVJTz31lPLy8vTwww/ryJEj6tKlixYvXqyQkBBPnS4AAAAAADVPaVgjlQQ0rrCmSDp+iREPhjQnys7O1qxZs9SkSRNFREQoNzdXvXv3Vo8ePbRixQr5+PhowoQJrsuj/vjHP6phw4YaPny40tLSXMf5y1/+otdff13//Oc/1aFDB73zzju65pprtGnTJjVt2tTVb/To0Xr99dc1c+ZM+fv766233tILL7ygKVOmqEOHDlq3bp3uu+8+BQcHa9iwYWd1bpap7PVAOG1ZWVkKCwtTZmYm69UAAAAAADyuos+h+fn5SklJUWJi4plt6mPMb7s6lbNOjYdDmrvuusttM6KcnBzFxsbqyy+/VMeOHfXOO+9o0qRJ2rJli2u2TGFhocLDwzVv3jz1799f8+bN0/XXX++2LEp8fLxGjBihZ5991tXWuXNnXXLJJfr73/+unTt3KjExUZMnT9bjjz/u6tOgQQO9+uqruvXWW11tEyZM0IIFC7Rq1apyz6Gyr3+NnlEDAAAAAABqiNKZNScGNTZbtcyk6d27t6ZNmyapZKmUqVOnasCAAfr++++1Zs0abd++vcxVMPn5+a61aU6UlZWlffv2qVu3bm7t3bp1048//ujWdvHFF7v+fODAAe3evVv33HOP7rvvPld7cXGxwsLCzuocJYIaAADOeQeOHdB/NvxH6/f+pNyiPIUFhKhbo67q07KPgv2DvV0eAAA4nzjKWUjXOEvCG7tnI4bg4GA1adLEdb9Tp04KCwvTW2+9JafTqU6dOmnWrFllnle3bt0Kj1s6A6eUMaZMW3Dw779Tle4o/dZbb6lLly5u/c50Y6TjEdQAAHCOyszN1JtfT1bSz18rsFa46oRHycfuqwO5+5W88h96c+lfdXPHG3Vf93vl4+FfnAAAwAXgxIWDS5WuWSN5PKw5nmVZstlsysvLU8eOHTVnzhxFRUVVeumR0NBQxcXFaeXKlbr88std7atWrVLnzp1P+rzo6GjFx8drx44duu222876PE7Eb20AAJyDDucc1oiPH9XB4mPq1PYPCgqo5d6h/kU6euyQPl7/qXYc3KFXrn1ZvtW0uB8AADgPlbe7k93n9zVrqiGsKSgocO0CfeTIEU2ZMkXZ2dkaNGiQOnfurNdee03XXnutaxen1NRUzZ07V08++aTq1atX7jGffPJJvfDCC2rcuLHat2+vmTNnav369eXOzDneuHHj9Nhjjyk0NFQDBgxQQUGBfvjhBx05csS1YdGZIqgBAOAc43Q69fTcZ3TYmaeOLXqUmZpbKjwkQp3b/EHfb1yqvy2bolF9n6jmSgEAwHmhoi24y9sNSvJIWLNw4ULFxsZKkkJCQtSiRQv961//Uq9evSRJK1as0OjRozV48GAdO3ZM8fHx6tOnT4UzbB577DFlZWXp//7v/5SRkaFWrVpp/vz5bjs+lefee+9VUFCQXnvtNT311FMKDg5WmzZtNHLkyLM+T3Z98iB2fQIAeEJySrKe+Owpde1wpWy2U18HnZ2XpfUbl+vzB+cqolZENVQIAAC8pcp3faoopCl14m5QliQf/2q9DOpcUNnX31aNNQEAgCowd/1nqhMRV6mQRpJqBYYqIDhM/9n4Hw9XBgAAzitOx6lDGun33aBKg5nSmTXGWS1lnm8IagAAOIcUO4q1YvtKNYhufFrPi46sp6+3LvVQVQAA4Lxk2dwvcapovbsTwxofv5Ln47QxDwkAgHNITkGOip3FCvQLOq3nBfgF6dDB/R6qCgAAnJcsS7L7SjZ7ya0y/UvDmkrO/EVZxFsAAJxDfO2+sskmx2lOJXY6HQrw9fdQVQAA4LxlWacXupxuf5RBUAMAwDkk0C9QceGxOnAk7bSedzBzv5pHNfNQVQAAAKgqBDUAAJxDLMvSzR1v1L6MlEo/x+F06PChfbq+/XWeKwwAAABVgqAGAIBzzBUXXaHivFxlHNlXqf7b92xWw/D6ale/nYcrAwAAwNkiqAEA4BwTHhSu568ao63b15zyEqgd+7Yq8+A+jbv6eVmWVU0VAgAA4Eyx6xMAAOegPi37yGmceuk/r2h3cJjqxzZRZFi0LMuS0+nQvoOp2pexUwFOm/5282Q1jW7q7ZIBAABQCQQ1AACco/q16qfWca315YavNHf9Z9qc/z/ZbT4qdhSpfni8Rlx6t/q16qfQwFBvlwoAAIBKIqgBAOAcFhseq/t63Kthl92p/Vn7lV+UryC/IMWGxcpm4wpnAABw/pg6dapee+01paWl6aKLLtLkyZPVo0cPb5dV5fgNDgCA84Cfj5/q16mvptFNFV87npAGAAB4jjGSo1gqLiz5rzEeH3LOnDkaOXKkxowZo3Xr1qlHjx4aMGCAUlNTPT52deO3OAAAAAAAUDnFRVL+MakgRyrMK/lv/rGSdg964403dM899+jee+9Vy5YtNXnyZNWvX1/Tpk3z6LjeQFADAAAAAABOrbhIKswtO4PGmJJ2D4U1hYWFWrNmjfr37+/W3r9/f61atcojY3oTQQ0AAAAAAKiYMVJRXsV9ivI8chnUwYMH5XA4FB0d7dYeHR2t9PT0Kh/P2whqAAAAAABAxZyOU4cwxpT08xDLsk4YzpRpOx8Q1AAAAAAAgIoZZ9X2Ow2RkZGy2+1lZs9kZGSUmWVzPiCoAQAAAAAAFbMqGR9Utt9p8PPzU6dOnZSUlOTWnpSUpK5du1b5eN7m4+0CAAAAAABADWezS5ZV8eVPllXSzwNGjRqlO+64QxdffLEuu+wyTZ8+XampqXrwwQc9Mp43EdQAAAAAAICKWZbkG1iyu9PJ+AaW9POAW265RYcOHdKLL76otLQ0tW7dWgsWLFBCQoJHxvMmghoAAAAAAHBqPr6Sgsru7lQa4vj4enT4hx9+WA8//LBHx6gJCGoAAAAAAEDl+PhKdp/fdoFylqxJU3pZFKoEQQ0AAAAAAKg8yyoJa+AR7PoEAAAAAABQQxDUAAAAAAAA1BAENQAAAAAAADUEQQ0AAAAAAEANQVADAAAAAABQQxDUAAAAAAAA1BAENQAAAAAAADUEQQ0AAAAAAEANQVADAAAAAAAqzRgjk3tM5tjhkv8a49HxJk6cqEsuuUQhISGKiorSddddp61bt3p0TG8iqAEAAAAAAJViso9IOzdI+36R9qeU/HfnhpJ2D1m+fLlGjBih7777TklJSSouLlb//v2Vk5PjsTG9ycfbBQAAAAAAgJrPZB+R0neUfcBRJKXvkIlpJKtW7Sofd+HChW73Z86cqaioKK1Zs0aXX355lY/nbcyoAQAAAAAAFTLGSAd2V9zp4G6PXwYlSZmZmZKkOnXqeHwsbyCoAQAAAAAAFcvLLpk5U5HiopJ+HmSM0ahRo9S9e3e1bt3ao2N5C5c+AQAAAACAip0qpDndfmfokUce0U8//aSVK1d6dBxvYkYNAMAjTPHp/U/6dPsDAACgGtl9q7bfGXj00Uc1f/58LV26VPXq1fPYON5GUAMAqHImY6/MFzNl9u+pXP+9KTJfvCtzaL+HKwOAc5MxRsY4PdYfAE4psNapQxgf35J+VcwYo0ceeURz587VN998o8TExCofoybh0icAQJUyRw7IufxzqbhIZvnnsvW8Vlb0yb/xMHtT5Fz5peR0Sss+k/rdIiu06ncLAIBzlTFGKsiRigpkgsNl2ez6Jf0XLdz0H+0+skfFzmLVDgzX5c16qlvjbvKx+0h5xyRH8W/9+W4WwNmzLEumbv3yd30qFVlflmVV+dgjRozQRx99pM8//1whISFKT0+XJIWFhSkwMLDKx/M2/tUGAFSt0Nq/BzOOYjmXf37SmTVuIY0kxSRItcKqqVAAOEcU5kn5OZKjWL/uWqfHP35UT879o7KKjqpDoza6tNnFiqhdW++selt3vHO75n8/R6Ygt2SdiJyj1bIDC4ALg1WrthTTqOzMGh9fyUNbc0vStGnTlJmZqV69eik2NtZ1mzNnjkfG8zZm1AAAqpRl95G6XSV9u0Bmb4orrDlxZs2JIY3VoJmsy67gm18AOJGvv1SQq037Nmls0iT1bt1LD/S/V4F+7t8iX9n2Cm1K3aAP/vuhDmSm6e5LbpXlH+iRb7cBXLisWrVlgsN/3wXKXnK5kyf/rbnQAmd+GwYAVDnL7iOr21Wy4n+7fviEmTWENABQeZbNroNyaNzXb+jai6/R1e2uVIDNx+2Di5FkOR1qHddcf7zqcf1n+1It2vk/WX7n3yUBALzPsixZQSGyQuqU/JdAuErxGzEAwCNOFtY41/2XkAYATtN/Ni5Sw9jGurxFt5IG4yxZg8YYGUlyFEtOhySpbkikbul6i+as/+yC+xYaAM4H/FYMAPCY8sIa8/NaQhoAOA1FxUVasPEr9W7Vs+QSg9Jvro2z5LKD40IaSZLdVx0adlBOUbbWpq71TtEAgDPGb8YAAI8qDWt0wpRYKyCIkAYAKuGX/b/IYRxqGdei5PICt7DGlAlpLJtNPnYfdUrsoO92fOedogEAZ4zfjgEAnpe+u+TDxHFMYb50YJ+XCgKAc0d2QbbCgsJca0C4wpoT2Xzcwu/woHBl5WVVV5kAgCpCUAMA8CjXwsEncjor3LobAFDCz8dPhcWFrvtGcp9F43rA6bYmTaGjUP6+/p4vEABQpQhqAAAeU97uTrabHj7pblAAgLLq166vw9mHdSj7cJmFg90ct8CwJP2Stl2JEYnVWisA4OwR1AAAPOKkW3D7+Fa4dTcAwF1kSKQ6N+yiFT//t9yFg+Xjd8ICw8Xae3ifUg+mqm+rvt4pGgBwxghqAABV7qQhzW9rJ5xs627CGgAo36B2g7Ryy0odzMr4vfG3hYNPXGDYOB2al/yZejbtqZCAEC9VDAA4UwQ1AIAqZQ5nVBjSlDppWJN5uLpLBoAazRijdhGN1C+xm/66aKoysg64QppSpWFNsdOh97/9SIePHNS9HW6UKe8SKQBAjUZQAwCoWrUjZTVoJunkIU2pE8MaK7GlFFq72koFgHNCQa6sonzd3+UOXV6/i1758g19tHqO9h7e6+qSU5CjJZu+1ovzX9OBwxl69aoxCvUNlHKOui0wDABVwTidMvt2yLn9R5l9O2R++4LOU8aNGyfLstxuMTExHh3Tm3y8XQAA4PxiWTbp0n6yImOlxq1PGtK4+tt9pG5XyUrZUtK/dJ0FAEAJvwCpKF82Feu+Pzys3h2u0ZcbvtSkL9+UZGS32ZVfmK9WsRfpvm7367JGXeSbl10yszGgFv+uAqhSZsdGOVd9KeVkldyXpOBQ2bpeLatRa4+Ne9FFF2nJkiWu+3a73WNjeRtBDQCgylmWTWratvL97T5SkzYerAgAzl2WzS4THC45imT5BqhpdFM9Ef2EHrz8QR3MPqhiR7HCgsJUJ7iO6znG7is5imWxPTeAKmR2bJQz6aOyD+RkyZn0kWz9hnosrPHx8TmvZ9Ecj0ufAAAAgBrOstll+Qa4tQX6Bap+nfpKrJvoFtL83p+QBkDVMU5nyUyaCjhXfeWxy6C2bdumuLg4JSYmasiQIdqxY4dHxqkJakxQM3HiRFmWpZEjR7ra5s6dqyuuuEKRkZGyLEvr16+v1LEmT56s5s2bKzAwUPXr19cTTzyh/Px8tz5Tp05VYmKiAgIC1KlTJ/33v/91e/yuu+4qcw3cpZdeeranCQAAAADAuSd9p+typ5PKySzpV8W6dOmi999/X4sWLdJbb72l9PR0de3aVYcOHarysWqCGhHUJCcna/r06Wrb1n2afE5Ojrp166Y//elPlT7WrFmz9PTTT+uFF17Qli1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"text/plain": [ "
" ] @@ -1005,9 +1104,25 @@ "plt.show()\n" ] }, + { + "cell_type": "markdown", + "id": "f8376a58-2f50-4332-a538-1a16b2222541", + "metadata": {}, + "source": [ + "Similar to the pickup points, the dropoff centermost location points seem to show some variation before and after the program as well. This implies that the dropoff clusters identified by the K-Means algorithms are not identical which indicates some shift in the behavior of customers in terms of where they take rides to in Hyde Park before and after the program." + ] + }, + { + "cell_type": "markdown", + "id": "d7242a3e-61c6-424d-9a42-9d3a93f0fccc", + "metadata": {}, + "source": [ + "It could be beneficial to check the location addresses of these centermost points to see if any patterns emerge." + ] + }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 38, "id": "27dda7d4-b591-4f34-94e5-b4a1d77601fa", "metadata": {}, "outputs": [], @@ -1024,7 +1139,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 74, "id": "69877b90-e8f9-42a0-baf0-37352e92cc81", "metadata": {}, "outputs": [], @@ -1039,7 +1154,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 41, "id": "a7a44770-79be-4f28-96ce-8ae13bd02d45", "metadata": {}, "outputs": [ @@ -1047,51 +1162,116 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 4922 S Cornell Ave, Chicago, IL 60615, USA\n", - "1 5746 S Ellis Ave, Chicago, IL 60637, USA\n", - "2 6134 S University Ave, Chicago, IL 60637, USA\n", - "3 1455 E 54th St, Chicago, IL 60615, USA\n", - "4 1208 E 47th St, Chicago, IL 60653, USA\n", - "5 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", - "6 QCM8+6W Chicago, IL, USA\n", - "7 4454 S Drexel Blvd, Chicago, IL 60653, USA\n", - "8 5719 S Kimbark Ave, Chicago, IL 60637, USA\n", - "9 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", - "10 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", - "11 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", - "12 6124 S Ingleside Ave, Chicago, IL 60637, USA\n", - "13 5758 S Blackstone Ave, Chicago, IL 60637, USA\n", - "14 1120 E 54th St, Chicago, IL 60615, USA\n", + "Pickup centerpoints before the program: \n", + "0 1455 E 54th St, Chicago, IL 60615, USA\n", + "1 5746 S Ellis Ave, Chicago, IL 60637, USA\n", + "2 3931 S Michigan Ave, Chicago, IL 60653, USA\n", + "3 1322 E 54th St, Chicago, IL 60615, USA\n", + "4 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", + "5 5437 S E View Park, Chicago, IL 60615, USA\n", + "6 QCM8+6W Chicago, IL, USA\n", + "7 5719 S Kimbark Ave, Chicago, IL 60637, USA\n", + "8 6134 S University Ave, Chicago, IL 60637, USA\n", + "9 6122 S Kenwood Ave, Chicago, IL 60637, USA\n", + "10 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", + "11 5120 S Ellis Ave, Chicago, IL 60615, USA\n", + "12 4858 S Greenwood Ave, Chicago, IL 60615, USA\n", + "13 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", + "14 6501 S St Lawrence Ave, Chicago, IL 60637, USA\n", "Name: center_address, dtype: object\n", - "0 6134 S University Ave, Chicago, IL 60637, USA\n", + "\n", + "Pickup centerpoints after the program: \n", + "0 5746 S Ellis Ave, Chicago, IL 60637, USA\n", "1 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", - "2 1120 E 54th St, Chicago, IL 60615, USA\n", - "3 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", - "4 1455 E 54th St, Chicago, IL 60615, USA\n", - "5 5746 S Ellis Ave, Chicago, IL 60637, USA\n", - "6 5700 S DuSable Lk Shr Dr, Chicago, IL 60637, USA\n", - "7 4454 S Drexel Blvd, Chicago, IL 60653, USA\n", - "8 1208 E 47th St, Chicago, IL 60653, USA\n", - "9 4922 S Cornell Ave, Chicago, IL 60615, USA\n", + "2 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", + "3 1208 E 47th St, Chicago, IL 60653, USA\n", + "4 5120 S Ellis Ave, Chicago, IL 60615, USA\n", + "5 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", + "6 6817 S Champlain Ave, Chicago, IL 60637, USA\n", + "7 5437 S E View Park, Chicago, IL 60615, USA\n", + "8 4922 S Cornell Ave, Chicago, IL 60615, USA\n", + "9 6122 S Kenwood Ave, Chicago, IL 60637, USA\n", "10 5719 S Kimbark Ave, Chicago, IL 60637, USA\n", - "11 1322 E 54th St, Chicago, IL 60615, USA\n", - "12 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", - "13 5437 S E View Park, Chicago, IL 60615, USA\n", - "14 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", + "11 1455 E 54th St, Chicago, IL 60615, USA\n", + "12 1322 E 54th St, Chicago, IL 60615, USA\n", + "13 6134 S University Ave, Chicago, IL 60637, USA\n", + "14 5700 S DuSable Lk Shr Dr, Chicago, IL 60637, USA\n", "Name: center_address, dtype: object\n" ] } ], "source": [ "# Compare before and after center point locations for pickup\n", - "\n", + "print('Pickup centerpoints before the program: ')\n", "print(before_pickup_centers['center_address'])\n", + "\n", + "print('\\nPickup centerpoints after the program: ')\n", "print(after_pickup_centers['center_address'])" ] }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 52, + "id": "48e01dcb-16ad-4534-a29e-25c097c09a93", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Addresses present in after pickup centerpoints but not in before pickup centerpoints:\n", + "5700 S DuSable Lk Shr Dr, Chicago, IL 60637, USA\n", + "1208 E 47th St, Chicago, IL 60653, USA\n", + "4922 S Cornell Ave, Chicago, IL 60615, USA\n", + "6817 S Champlain Ave, Chicago, IL 60637, USA\n", + "\n", + "Common addresses in both after pickup centerpoints and before pickup centerpoints:\n", + "5719 S Kimbark Ave, Chicago, IL 60637, USA\n", + "5437 S E View Park, Chicago, IL 60615, USA\n", + "6358 S Kimbark Ave, Chicago, IL 60637, USA\n", + "6122 S Kenwood Ave, Chicago, IL 60637, USA\n", + "5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", + "1322 E 54th St, Chicago, IL 60615, USA\n", + "5120 S Ellis Ave, Chicago, IL 60615, USA\n", + "6134 S University Ave, Chicago, IL 60637, USA\n", + "1455 E 54th St, Chicago, IL 60615, USA\n", + "5746 S Ellis Ave, Chicago, IL 60637, USA\n", + "5534 S Dorchester Ave, Chicago, IL 60637, USA\n" + ] + } + ], + "source": [ + "# Convert the 'center_address' column to sets\n", + "set_df1 = set(before_pickup_centers['center_address'])\n", + "set_df2 = set(after_pickup_centers['center_address'])\n", + "\n", + "# Find addresses present in after_pickup_centers but not in before_pickup_centers\n", + "not_in_df1 = set_df2 - set_df1\n", + "\n", + "# Find common addresses in both before_pickup_centers and after_pickup_centers \n", + "common_addresses_p = set_df1.intersection(set_df2)\n", + "\n", + "print(\"Addresses present in after pickup centerpoints but not in before pickup centerpoints:\")\n", + "\n", + "for ele in not_in_df1:\n", + " print(ele)\n", + "\n", + "print(\"\\nCommon addresses in both after pickup centerpoints and before pickup centerpoints:\")\n", + "for ele in common_addresses_p:\n", + " print(ele)" + ] + }, + { + "cell_type": "markdown", + "id": "6f44fd22-f2e8-4920-b454-f6ae461c39af", + "metadata": {}, + "source": [ + "It appears that the cluster centerpoint locations that are present in both before and after program area clusters are on streets which surround the university or are next to it. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, "id": "c56608d0-a429-4f11-a07a-c269172241ae", "metadata": {}, "outputs": [ @@ -1099,37 +1279,40 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 1455 E 54th St, Chicago, IL 60615, USA\n", - "1 5437 S E View Park, Chicago, IL 60615, USA\n", - "2 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", - "3 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", - "4 1208 E 47th St, Chicago, IL 60653, USA\n", - "5 5746 S Ellis Ave, Chicago, IL 60637, USA\n", - "6 4922 S Cornell Ave, Chicago, IL 60615, USA\n", - "7 6133 S Langley Ave, Chicago, IL 60637, USA\n", - "8 1120 E 54th St, Chicago, IL 60615, USA\n", - "9 5719 S Kimbark Ave, Chicago, IL 60637, USA\n", - "10 QCM8+6W Chicago, IL, USA\n", - "11 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", - "12 5758 S Blackstone Ave, Chicago, IL 60637, USA\n", - "13 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", - "14 6134 S University Ave, Chicago, IL 60637, USA\n", + "Dropoff centerpoints before the program: \n", + "0 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", + "1 4922 S Cornell Ave, Chicago, IL 60615, USA\n", + "2 5700 S DuSable Lk Shr Dr, Chicago, IL 60637, USA\n", + "3 1455 E 54th St, Chicago, IL 60615, USA\n", + "4 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", + "5 5437 S E View Park, Chicago, IL 60615, USA\n", + "6 4858 S Greenwood Ave, Chicago, IL 60615, USA\n", + "7 5719 S Kimbark Ave, Chicago, IL 60637, USA\n", + "8 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", + "9 6454 S Drexel Ave, Chicago, IL 60637, USA\n", + "10 5746 S Ellis Ave, Chicago, IL 60637, USA\n", + "11 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", + "12 1322 E 54th St, Chicago, IL 60615, USA\n", + "13 1120 E 54th St, Chicago, IL 60615, USA\n", + "14 QCGC+F2 Chicago, IL, USA\n", "Name: center_address, dtype: object\n", - "0 6124 S Ingleside Ave, Chicago, IL 60637, USA\n", - "1 1455 E 54th St, Chicago, IL 60615, USA\n", - "2 1208 E 47th St, Chicago, IL 60653, USA\n", - "3 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", - "4 5746 S Ellis Ave, Chicago, IL 60637, USA\n", - "5 6134 S University Ave, Chicago, IL 60637, USA\n", + "\n", + "Dropoff centerpoints after the program: \n", + "0 5746 S Ellis Ave, Chicago, IL 60637, USA\n", + "1 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", + "2 6358 S Kimbark Ave, Chicago, IL 60637, USA\n", + "3 4922 S Cornell Ave, Chicago, IL 60615, USA\n", + "4 6134 S University Ave, Chicago, IL 60637, USA\n", + "5 5700 S DuSable Lk Shr Dr, Chicago, IL 60637, USA\n", "6 5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", - "7 QCGC+F2 Chicago, IL, USA\n", - "8 5719 S Kimbark Ave, Chicago, IL 60637, USA\n", - "9 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", - "10 5700 S DuSable Lk Shr Dr, Chicago, IL 60637, USA\n", + "7 1120 E 54th St, Chicago, IL 60615, USA\n", + "8 1455 E 54th St, Chicago, IL 60615, USA\n", + "9 1208 E 47th St, Chicago, IL 60653, USA\n", + "10 6817 S Champlain Ave, Chicago, IL 60637, USA\n", "11 5758 S Blackstone Ave, Chicago, IL 60637, USA\n", - "12 5534 S Dorchester Ave, Chicago, IL 60637, USA\n", - "13 1120 E 54th St, Chicago, IL 60615, USA\n", - "14 1322 E 54th St, Chicago, IL 60615, USA\n", + "12 5437 S E View Park, Chicago, IL 60615, USA\n", + "13 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", + "14 6122 S Kenwood Ave, Chicago, IL 60637, USA\n", "Name: center_address, dtype: object\n" ] } @@ -1137,15 +1320,327 @@ "source": [ "# Compare before and after center point locations for dropoff\n", "\n", + "print('Dropoff centerpoints before the program: ')\n", "print(before_dropoff_centers['center_address'])\n", + "\n", + "print('\\nDropoff centerpoints after the program: ')\n", "print(after_dropoff_centers['center_address'])" ] }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 59, + "id": "c2f36410-0dff-450c-aaf9-a02c71ceefdc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Addresses present in after dropoff centerpoints but not in before pickup centerpoints:\n", + "1208 E 47th St, Chicago, IL 60653, USA\n", + "6817 S Champlain Ave, Chicago, IL 60637, USA\n", + "6122 S Kenwood Ave, Chicago, IL 60637, USA\n", + "6134 S University Ave, Chicago, IL 60637, USA\n", + "5758 S Blackstone Ave, Chicago, IL 60637, USA\n", + "\n", + "Common addresses in both after dropoff centerpoints and before pickup centerpoints:\n", + "5746 S Ellis Ave, Chicago, IL 60637, USA\n", + "5437 S E View Park, Chicago, IL 60615, USA\n", + "6358 S Kimbark Ave, Chicago, IL 60637, USA\n", + "5700 S DuSable Lk Shr Dr, Chicago, IL 60637, USA\n", + "4922 S Cornell Ave, Chicago, IL 60615, USA\n", + "5142 S Hyde Park Blvd, Chicago, IL 60615, USA\n", + "5401-5405 S Drexel Blvd, Chicago, IL 60615, USA\n", + "1455 E 54th St, Chicago, IL 60615, USA\n", + "1120 E 54th St, Chicago, IL 60615, USA\n", + "5534 S Dorchester Ave, Chicago, IL 60637, USA\n" + ] + } + ], + "source": [ + "# Convert the 'center_address' column to sets\n", + "set_df1 = set(before_dropoff_centers['center_address'])\n", + "set_df2 = set(after_dropoff_centers['center_address'])\n", + "\n", + "# Find addresses present in after_pickup_centers but not in before_pickup_centers\n", + "not_in_df1 = set_df2 - set_df1\n", + "\n", + "# Find common addresses in both before_pickup_centers and after_pickup_centers \n", + "common_addresses_d = set_df1.intersection(set_df2)\n", + "\n", + "print(\"Addresses present in after dropoff centerpoints but not in before pickup centerpoints:\")\n", + "\n", + "for ele in not_in_df1:\n", + " print(ele)\n", + "\n", + "print(\"\\nCommon addresses in both after dropoff centerpoints and before pickup centerpoints:\")\n", + "for ele in common_addresses_d:\n", + " print(ele)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "260a9301-9403-4570-8f20-87b1f93e172d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['center_pickup_cluster', 'center_coordinates', 'center_lon',\n", + " 'center_lat', 'center_address', 'cluster_id'],\n", + " dtype='object')" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "before_pickup_centers.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "b1c7c077-a1d7-4dd5-88c9-87b7d5ac8f6d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " center_address \\\n", + "0 1455 E 54th St, Chicago, IL 60615, USA \n", + "1 5746 S Ellis Ave, Chicago, IL 60637, USA \n", + "2 1322 E 54th St, Chicago, IL 60615, USA \n", + "3 6358 S Kimbark Ave, Chicago, IL 60637, USA \n", + "4 5437 S E View Park, Chicago, IL 60615, USA \n", + "5 5719 S Kimbark Ave, Chicago, IL 60637, USA \n", + "6 6134 S University Ave, Chicago, IL 60637, USA \n", + "7 6122 S Kenwood Ave, Chicago, IL 60637, USA \n", + "8 5534 S Dorchester Ave, Chicago, IL 60637, USA \n", + "9 5120 S Ellis Ave, Chicago, IL 60615, USA \n", + "10 5142 S Hyde Park Blvd, Chicago, IL 60615, USA \n", + "\n", + " center_pickup_cluster_before center_pickup_cluster_after \n", + "0 0 11 \n", + "1 1 0 \n", + "2 3 12 \n", + "3 4 5 \n", + "4 5 7 \n", + "5 7 10 \n", + "6 8 13 \n", + "7 9 9 \n", + "8 10 1 \n", + "9 11 4 \n", + "10 13 2 \n" + ] + } + ], + "source": [ + "# Merge DataFrames on index (cluster_id) to compare pickup cluster IDs\n", + "before_pickup_centers.reset_index(inplace=True, drop=True)\n", + "after_pickup_centers.reset_index(inplace=True, drop=True)\n", + "\n", + "columns_to_keep = ['center_address','center_pickup_cluster_before', 'center_pickup_cluster_after']\n", + "\n", + "p_merged_df = pd.merge(\n", + " before_pickup_centers, \n", + " after_pickup_centers, \n", + " on='center_address', \n", + " how='inner', \n", + " suffixes=('_before', '_after')\n", + ")[columns_to_keep]\n", + "\n", + "print(p_merged_df)\n" + ] + }, + { + "cell_type": "markdown", + "id": "c476b3d9-130f-4452-adee-41c3ca256ff0", + "metadata": {}, + "source": [ + "The common drop off centerpoint locations in the before and after clusters are also following a similar trend to the pickup locations as they are also within the university premises or nearby." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "96983242-ea55-4457-b6e5-a490a06c2840", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " center_address \\\n", + "0 5534 S Dorchester Ave, Chicago, IL 60637, USA \n", + "1 4922 S Cornell Ave, Chicago, IL 60615, USA \n", + "2 5700 S DuSable Lk Shr Dr, Chicago, IL 60637, USA \n", + "3 1455 E 54th St, Chicago, IL 60615, USA \n", + "4 6358 S Kimbark Ave, Chicago, IL 60637, USA \n", + "5 5437 S E View Park, Chicago, IL 60615, USA \n", + "6 5401-5405 S Drexel Blvd, Chicago, IL 60615, USA \n", + "7 5746 S Ellis Ave, Chicago, IL 60637, USA \n", + "8 5142 S Hyde Park Blvd, Chicago, IL 60615, USA \n", + "9 1120 E 54th St, Chicago, IL 60615, USA \n", + "\n", + " center_dropoff_cluster_before center_dropoff_cluster_after \n", + "0 0 1 \n", + "1 1 3 \n", + "2 2 5 \n", + "3 3 8 \n", + "4 4 2 \n", + "5 5 12 \n", + "6 8 13 \n", + "7 10 0 \n", + "8 11 6 \n", + "9 13 7 \n" + ] + } + ], + "source": [ + "# finding the clusters which have the same centerpoint locations\n", + "\n", + "# Merge DataFrames on index (cluster_id) to compare pickup cluster IDs\n", + "before_dropoff_centers.reset_index(inplace=True, drop=True)\n", + "after_dropoff_centers.reset_index(inplace=True, drop=True)\n", + "\n", + "columns_to_keep = ['center_address','center_dropoff_cluster_before', 'center_dropoff_cluster_after']\n", + "\n", + "d_merged_df = pd.merge(\n", + " before_dropoff_centers, \n", + " after_dropoff_centers, \n", + " on='center_address', \n", + " how='inner', \n", + " suffixes=('_before', '_after')\n", + ")[columns_to_keep]\n", + "\n", + "print(d_merged_df)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d2240d48-8483-472e-ac26-7bcad3af7ac3", + "metadata": {}, + "source": [ + "## 3) Before and after program clusters analysed using trip duration as a metric" + ] + }, + { + "cell_type": "code", + "execution_count": 61, "id": "99417bc1-36aa-425f-a0eb-642a4439e9a7", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Median trip durations before program:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+--------------------+\n", + "|pickup_cluster|median_trip_duration|\n", + "+--------------+--------------------+\n", + "| 0| 0.0|\n", + "| 1| 0.0|\n", + "| 2| 15.0|\n", + "| 3| 0.0|\n", + "| 4| 0.0|\n", + "| 5| 0.0|\n", + "| 6| 0.0|\n", + "| 7| 0.0|\n", + "| 8| 0.0|\n", + "| 9| 0.0|\n", + "| 10| 0.0|\n", + "| 11| 0.0|\n", + "| 12| 0.0|\n", + "| 13| 0.0|\n", + "| 14| 15.0|\n", + "+--------------+--------------------+\n", + "\n", + "Median trip durations after program:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 10811:===================================> (34 + 16) / 50]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+--------------------+\n", + "|pickup_cluster|median_trip_duration|\n", + "+--------------+--------------------+\n", + "| 0| 0.0|\n", + "| 1| 0.0|\n", + "| 2| 0.0|\n", + "| 3| 0.0|\n", + "| 4| 0.0|\n", + "| 5| 0.0|\n", + "| 6| 15.0|\n", + "| 7| 0.0|\n", + "| 8| 0.0|\n", + "| 9| 0.0|\n", + "| 10| 0.0|\n", + "| 11| 0.0|\n", + "| 12| 0.0|\n", + "| 13| 0.0|\n", + "| 14| 0.0|\n", + "+--------------+--------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "# Find ride trip duration changes before and after program based on pickup clusters\n", + "\n", + "before_pickup_median_duration = before.groupBy('pickup_cluster').agg(\n", + " F.expr('percentile_approx(trip_duration, 0.5)').alias('median_trip_duration')\n", + ").orderBy('pickup_cluster')\n", + "\n", + "after_pickup_median_duration = after.groupBy('pickup_cluster').agg(\n", + " F.expr('percentile_approx(trip_duration, 0.5)').alias('median_trip_duration')\n", + ").orderBy('pickup_cluster')\n", + "\n", + "print('Median trip durations before program:')\n", + "before_pickup_median_duration.show()\n", + "\n", + "print('Median trip durations after program:')\n", + "after_pickup_median_duration.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "c9a44631-f249-409c-8bb7-7d7be75e7a65", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1168,21 +1663,21 @@ "+--------------+---------------------+\n", "|pickup_cluster|average_trip_duration|\n", "+--------------+---------------------+\n", - "| 12| 5.889822766307327|\n", - "| 1| 2.488675938360686|\n", - "| 13| -0.2668360864040661|\n", - "| 6| 4.008822003113648|\n", - "| 3| 3.0738028416067356|\n", - "| 5| 5.083000499251123|\n", - "| 9| 5.054984036892515|\n", - "| 4| 4.514186295503212|\n", - "| 8| 1.1409299655568312|\n", - "| 7| 5.152899126290707|\n", - "| 10| 4.507743673194546|\n", - "| 11| 3.673683643803166|\n", - "| 14| 1.4098017088505561|\n", - "| 2| 5.398172323759791|\n", - "| 0| 3.5873064540855433|\n", + "| 0| 3.790178135278601|\n", + "| 1| 3.4030864299522494|\n", + "| 2| 5.386956521739131|\n", + "| 3| 3.5917224422893046|\n", + "| 4| 5.947109152190822|\n", + "| 5| 5.3751345532831|\n", + "| 6| 4.693183224050476|\n", + "| 7| 2.035257927117842|\n", + "| 8| 5.779142339440306|\n", + "| 9| 3.4394904458598727|\n", + "| 10| 5.163105046343976|\n", + "| 11| 3.972463911409927|\n", + "| 12| 4.9211654894046415|\n", + "| 13| 3.795847320651498|\n", + "| 14| 7.997772828507795|\n", "+--------------+---------------------+\n", "\n", "Average trip durations after program:\n" @@ -1192,7 +1687,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Stage 68583:=================================================> (44 + 3) / 47]\r" + "[Stage 10797:==============================================> (28 + 4) / 32]\r" ] }, { @@ -1202,21 +1697,21 @@ "+--------------+---------------------+\n", "|pickup_cluster|average_trip_duration|\n", "+--------------+---------------------+\n", - "| 12| 5.933034317304683|\n", + "| 0| 4.838796923653058|\n", "| 1| 5.011933510613307|\n", - "| 13| 5.364905177924569|\n", - "| 6| 5.379129328172225|\n", - "| 3| 5.23251788599123|\n", - "| 5| 4.838796923653058|\n", - "| 9| 5.577063550036523|\n", - "| 4| 4.933941452048683|\n", - "| 8| 5.47335072679836|\n", - "| 7| 7.1022727272727275|\n", + "| 2| 5.23251788599123|\n", + "| 3| 5.566561352574503|\n", + "| 4| 4.610894941634241|\n", + "| 5| 6.340273416743153|\n", + "| 6| 7.945804195804196|\n", + "| 7| 5.364905177924569|\n", + "| 8| 5.577063550036523|\n", + "| 9| 5.455318184393948|\n", "| 10| 4.648405828859428|\n", - "| 11| 4.880330722367276|\n", - "| 14| 4.936453228499111|\n", - "| 2| 4.366875084341685|\n", - "| 0| 5.608800643949557|\n", + "| 11| 4.933941452048683|\n", + "| 12| 4.880330722367276|\n", + "| 13| 5.496006075265793|\n", + "| 14| 5.379129328172225|\n", "+--------------+---------------------+\n", "\n" ] @@ -1230,10 +1725,10 @@ } ], "source": [ - "# Find ride trip duration changes before and after program based on pickup clusters\n", - "\n", - "before_pickup_avg_time = before.groupBy('pickup_cluster').agg(F.avg('trip_duration').alias('average_trip_duration'))\n", - "after_pickup_avg_time = after.groupBy('pickup_cluster').agg(F.avg('trip_duration').alias('average_trip_duration'))\n", + "before_pickup_avg_time = before.groupBy('pickup_cluster').agg(F.avg('trip_duration').alias('average_trip_duration'))\\\n", + " .orderBy('pickup_cluster')\n", + "after_pickup_avg_time = after.groupBy('pickup_cluster').agg(F.avg('trip_duration').alias('average_trip_duration'))\\\n", + " .orderBy('pickup_cluster')\n", "\n", "print('Average trip durations before program:')\n", "before_pickup_avg_time.show()\n", @@ -1242,6 +1737,81 @@ "after_pickup_avg_time.show()" ] }, + { + "cell_type": "markdown", + "id": "f989db12-be9e-4d52-97e2-9277bd572ed1", + "metadata": {}, + "source": [ + "Rohit: please check how the trip time is affected for the clusters with same addresses. " + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "511af7dc-74ca-4b16-a0ed-fea5af4b4532", + "metadata": {}, + "outputs": [], + "source": [ + "before_common = before.filter(F.col('pickup_cluster').isin(p_merged_df['center_pickup_cluster_before'].to_list()))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "d6c0a804-a908-4cb6-be9b-9c6503d6d0ad", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 10815:=================================================> (15 + 1) / 16]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+\n", + "|pickup_cluster|\n", + "+--------------+\n", + "| 11|\n", + "| 11|\n", + "| 11|\n", + "| 11|\n", + "| 11|\n", + "| 11|\n", + "| 11|\n", + "| 11|\n", + "| 11|\n", + "| 7|\n", + "| 7|\n", + "| 7|\n", + "| 7|\n", + "| 7|\n", + "| 7|\n", + "| 7|\n", + "| 7|\n", + "| 7|\n", + "| 7|\n", + "| 7|\n", + "+--------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "before_common.select('pickup_cluster').distinct().show()" + ] + }, { "cell_type": "code", "execution_count": 116,