{ "cells": [ { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RTTPlongitudelatitudeavg_onsavg_offsgeometry
021707 Myrtle\\Williams-106.47034731.7686410.2617190.496094POINT (-106.47035 31.76864)
121900 Magoffin\\Laurel-106.46748131.7680370.3607841.752941POINT (-106.46748 31.76804)
221931 Myrtle\\Eucalyptus-106.46791631.7702293.0195312.636719POINT (-106.46792 31.77023)
322023 Myrtle\\Willow-106.46669031.7709231.3085941.039062POINT (-106.46669 31.77092)
422114 Magoffin\\Walnut-106.46438731.7700180.1294120.074510POINT (-106.46439 31.77002)
........................
3424500Stanton St/Cincinnati Ave-106.50217231.7773961.6554252.766862POINT (-106.50217 31.77740)
3425500Stanton St/Kerbey Ave-106.49976931.7748241.0058562.035139POINT (-106.49977 31.77482)
3426500Stanton St/Missouri Ave-106.48891431.7624690.8856300.391496POINT (-106.48891 31.76247)
3427500Stanton St/Rim Rd-106.49662231.7711120.5666180.814056POINT (-106.49662 31.77111)
3428500Stanton\\Yandell-106.49029731.7640110.8020530.728739POINT (-106.49030 31.76401)
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3429 rows × 7 columns

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" ], "text/plain": [ " RT TP longitude latitude avg_ons \\\n", "0 2 1707 Myrtle\\Williams -106.470347 31.768641 0.261719 \n", "1 2 1900 Magoffin\\Laurel -106.467481 31.768037 0.360784 \n", "2 2 1931 Myrtle\\Eucalyptus -106.467916 31.770229 3.019531 \n", "3 2 2023 Myrtle\\Willow -106.466690 31.770923 1.308594 \n", "4 2 2114 Magoffin\\Walnut -106.464387 31.770018 0.129412 \n", "... ... ... ... ... ... \n", "3424 500 Stanton St/Cincinnati Ave -106.502172 31.777396 1.655425 \n", "3425 500 Stanton St/Kerbey Ave -106.499769 31.774824 1.005856 \n", "3426 500 Stanton St/Missouri Ave -106.488914 31.762469 0.885630 \n", "3427 500 Stanton St/Rim Rd -106.496622 31.771112 0.566618 \n", "3428 500 Stanton\\Yandell -106.490297 31.764011 0.802053 \n", "\n", " avg_offs geometry \n", "0 0.496094 POINT (-106.47035 31.76864) \n", "1 1.752941 POINT (-106.46748 31.76804) \n", "2 2.636719 POINT (-106.46792 31.77023) \n", "3 1.039062 POINT (-106.46669 31.77092) \n", "4 0.074510 POINT (-106.46439 31.77002) \n", "... ... ... \n", "3424 2.766862 POINT (-106.50217 31.77740) \n", "3425 2.035139 POINT (-106.49977 31.77482) \n", "3426 0.391496 POINT (-106.48891 31.76247) \n", "3427 0.814056 POINT (-106.49662 31.77111) \n", "3428 0.728739 POINT (-106.49030 31.76401) \n", "\n", "[3429 rows x 7 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transit_stops = gpd.read_file('C:/Users/jtrum/pennmusa/MUSA8010/repository/data/ridership.geojson')\n", "transit_stops = transit_stops.to_crs(epsg=4269)\n", "transit_stops" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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uniqueIDridershiptotalPophlPopwhitePopblackPopaiPopasianPopnhPopotherRacePop...major_road.nn3major_road.nn5stops_in_hexbays_in_hexcvIDpredictionerrorridership_per_stoppred_ridership_per_stopgeometry
010192.697819149.407509175.9152931.4630920.0000000.2797090.00000014.286663...0.0066130.0071210.00.0880.9988880.9988880.00.0POLYGON ((-106.63329 31.86642, -106.63579 31.8...
12038.91516730.19181735.4543010.2914790.0012660.0557240.0000002.917649...0.0057480.0066690.00.031.0004611.0004610.00.0POLYGON ((-106.63329 31.87508, -106.63579 31.8...
230175.428773136.018021160.1502511.3319740.0000000.2546420.00000013.006332...0.0027500.0036260.00.0640.9998770.9998770.00.0POLYGON ((-106.63079 31.86209, -106.63329 31.8...
340192.827021149.507686176.0332431.4640730.0000000.2798960.00000014.296242...0.0051560.0054150.00.0741.0007711.0007710.00.0POLYGON ((-106.63079 31.87075, -106.63329 31.8...
45071.35812557.91882155.4180410.0000000.1718090.0000000.0000009.697680...0.0043520.0053080.00.0771.0000091.0000090.00.0POLYGON ((-106.63079 31.87941, -106.63329 31.8...
..................................................................
211221150543.652444450.740202459.66626928.7937645.0127336.0466900.31411426.359382...0.0056340.0061340.00.0741.0006631.0006630.00.0POLYGON ((-106.22329 31.77115, -106.22579 31.7...
211321160543.602526450.698816459.62406328.7911205.0122726.0461350.31408526.356962...0.0024910.0030270.00.0610.9999080.9999080.00.0POLYGON ((-106.22329 31.77981, -106.22579 31.7...
211421170543.552597450.657419459.58184728.7884765.0118126.0455800.31405626.354541...0.0059170.0067910.00.0460.9996580.9996580.00.0POLYGON ((-106.22329 31.78847, -106.22579 31.7...
211521180543.364693450.305350459.40998028.8371005.0005816.0083570.31052626.292744...0.0036400.0043120.00.0121.0002741.0002740.00.0POLYGON ((-106.22329 31.79713, -106.22579 31.7...
211621190178.687388142.226068150.69083711.2315221.3609430.9272360.0000007.073915...0.0056180.0067060.00.0861.0006331.0006330.00.0POLYGON ((-106.22329 31.80579, -106.22579 31.8...
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2117 rows × 150 columns

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" ], "text/plain": [ " uniqueID ridership totalPop hlPop whitePop blackPop \\\n", "0 1 0 192.697819 149.407509 175.915293 1.463092 \n", "1 2 0 38.915167 30.191817 35.454301 0.291479 \n", "2 3 0 175.428773 136.018021 160.150251 1.331974 \n", "3 4 0 192.827021 149.507686 176.033243 1.464073 \n", "4 5 0 71.358125 57.918821 55.418041 0.000000 \n", "... ... ... ... ... ... ... \n", "2112 2115 0 543.652444 450.740202 459.666269 28.793764 \n", "2113 2116 0 543.602526 450.698816 459.624063 28.791120 \n", "2114 2117 0 543.552597 450.657419 459.581847 28.788476 \n", "2115 2118 0 543.364693 450.305350 459.409980 28.837100 \n", "2116 2119 0 178.687388 142.226068 150.690837 11.231522 \n", "\n", " aiPop asianPop nhPop otherRacePop ... major_road.nn3 \\\n", "0 0.000000 0.279709 0.000000 14.286663 ... 0.006613 \n", "1 0.001266 0.055724 0.000000 2.917649 ... 0.005748 \n", "2 0.000000 0.254642 0.000000 13.006332 ... 0.002750 \n", "3 0.000000 0.279896 0.000000 14.296242 ... 0.005156 \n", "4 0.171809 0.000000 0.000000 9.697680 ... 0.004352 \n", "... ... ... ... ... ... ... \n", "2112 5.012733 6.046690 0.314114 26.359382 ... 0.005634 \n", "2113 5.012272 6.046135 0.314085 26.356962 ... 0.002491 \n", "2114 5.011812 6.045580 0.314056 26.354541 ... 0.005917 \n", "2115 5.000581 6.008357 0.310526 26.292744 ... 0.003640 \n", "2116 1.360943 0.927236 0.000000 7.073915 ... 0.005618 \n", "\n", " major_road.nn5 stops_in_hex bays_in_hex cvID prediction error \\\n", "0 0.007121 0.0 0.0 88 0.998888 0.998888 \n", "1 0.006669 0.0 0.0 3 1.000461 1.000461 \n", "2 0.003626 0.0 0.0 64 0.999877 0.999877 \n", "3 0.005415 0.0 0.0 74 1.000771 1.000771 \n", "4 0.005308 0.0 0.0 77 1.000009 1.000009 \n", "... ... ... ... ... ... ... \n", "2112 0.006134 0.0 0.0 74 1.000663 1.000663 \n", "2113 0.003027 0.0 0.0 61 0.999908 0.999908 \n", "2114 0.006791 0.0 0.0 46 0.999658 0.999658 \n", "2115 0.004312 0.0 0.0 12 1.000274 1.000274 \n", "2116 0.006706 0.0 0.0 86 1.000633 1.000633 \n", "\n", " ridership_per_stop pred_ridership_per_stop \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "... ... ... \n", "2112 0.0 0.0 \n", "2113 0.0 0.0 \n", "2114 0.0 0.0 \n", "2115 0.0 0.0 \n", "2116 0.0 0.0 \n", "\n", " geometry \n", "0 POLYGON ((-106.63329 31.86642, -106.63579 31.8... \n", "1 POLYGON ((-106.63329 31.87508, -106.63579 31.8... \n", "2 POLYGON ((-106.63079 31.86209, -106.63329 31.8... \n", "3 POLYGON ((-106.63079 31.87075, -106.63329 31.8... \n", "4 POLYGON ((-106.63079 31.87941, -106.63329 31.8... \n", "... ... \n", "2112 POLYGON ((-106.22329 31.77115, -106.22579 31.7... \n", "2113 POLYGON ((-106.22329 31.77981, -106.22579 31.7... \n", "2114 POLYGON ((-106.22329 31.78847, -106.22579 31.7... \n", "2115 POLYGON ((-106.22329 31.79713, -106.22579 31.7... \n", "2116 POLYGON ((-106.22329 31.80579, -106.22579 31.8... \n", "\n", "[2117 rows x 150 columns]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_hex = gpd.read_file('C:/Users/jtrum/pennmusa/MUSA8010/repository/ElPaso-Bus-Network/final_hex4.geojson')\n", "final_hex = final_hex.to_crs(epsg=4269)\n", "final_hex" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "# Set max rows to be seen to 61\n", "pd.set_option('display.max_rows', 61)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "image/png": 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ridership_per_stop_leftpred_ridership_per_stop_leftwhitePop_leftblackPop_leftasianPop_lefthlPop_leftotherRacePop_leftnhPop_leftaiPop_leftdisability_leftmedHHInc_leftemploymentHHMix_left
RT
2134522.944788134534.933482208.2673929.8877992.213135309.05460597.9222920.000000e+003.22151532.5032982358.7142550.678890
499808.54059799805.952204294.02780817.7769142.035270391.922281102.2586380.000000e+004.39246539.8503783427.1745510.605265
539859.11212139831.697560300.47448811.7545953.375640313.01346026.1721512.725746e-017.29588314.6830082764.1411570.627341
670198.15974070151.561310323.78010511.2680053.375640347.71103642.2670876.911012e-011.86352218.8195532533.9827110.637560
7172704.875000172705.516626272.82512116.9376373.704804264.80907835.6946352.042944e-011.552696447.8441552364.8034310.613350
831700.78510931715.974935345.97654816.8266451.665794346.27693842.5705993.759443e-013.047165356.4734384101.1511770.672532
10180693.320513180688.863148293.62630026.5126542.220482338.46483886.2566460.000000e+004.6585110.9458814039.4598610.377877
11106647.419048106647.802261181.1202857.6504618.498604170.61597838.4390020.000000e+001.2808141.6306913272.1775970.555266
1285971.93968385869.967130217.1938772.9146743.969830246.26141155.4406964.049652e-010.811182127.0864993001.7171480.637096
13352065.832937351993.357497303.7955686.19387512.329514279.31028763.3631406.113203e+001.846071222.1354783202.5337210.680011
14622898.507567622629.520302260.6354859.2970098.992134256.00000561.4206341.733508e+001.573087184.4089193837.0760960.598012
15480979.640229480916.501601218.5308108.3602158.935973218.78784253.8060722.901344e+002.26899778.4770252929.1958470.702311
1661913.33968361832.045438173.2524591.4776532.870003157.17723923.9226235.762041e-010.83968169.4432863180.6960300.527821
1769928.57301669832.930591260.8910033.8447439.490762267.07052456.8134158.388609e-010.76467754.3778823760.0660770.720910
1992025.03968391947.301004278.9052304.53279911.618772262.23199753.8497981.771287e+000.69652562.9026544319.9786970.639357
2018662.66706318666.881909219.4729504.2802039.281274231.39708958.1006543.531956e-011.17946037.7191132729.7065790.842817
2137845.84074737849.810601333.3561751.8401531.113253378.65999664.3905574.420221e-021.224643273.7000063125.0749430.502860
24122421.171047122415.645083332.2463185.7069590.497646411.51769977.4205570.000000e+002.410588248.1168263082.6953370.430235
2585211.19393985213.359826248.0001342.7032310.746362273.51352637.5729682.290862e-011.245279156.3749372474.4818470.676294
2675149.27989575170.468097230.3633053.5974882.273301320.57272084.1376310.000000e+000.99889327.5901682350.3892830.626042
3289786.13311789819.274260391.89272310.9546683.338142418.89318750.2123304.634834e-071.410105248.5877443713.1230840.354143
33139203.941558139245.216131330.3840854.9149691.920133349.62460643.2652171.664783e-010.704070357.7499293100.0872030.557372
34233473.055575233521.007256348.55039713.6150404.468771396.17187073.1000730.000000e+004.031467176.7330303974.6800690.511730
35301469.718615301429.061166432.89257311.1408153.939210459.83485057.5649899.304611e-022.673003383.5057334821.6662970.560738
36223034.837876223033.080681341.59483813.8903183.574373375.28098058.4071370.000000e+002.991883345.2134723497.8437240.492910
37405965.933333405853.282068281.59056817.8359546.148458315.29916659.9500500.000000e+001.671791228.0817552734.9149910.426441
41100968.400000100992.533399307.23203115.6891655.209317333.19598048.1134330.000000e+001.188075330.6337502889.6428050.318899
43146544.766667146450.246174282.83404723.5796367.161005294.62517957.8382044.695201e-042.407509145.9370952935.8239320.390286
44191258.266667191192.397839351.66621242.5916377.176825348.84815951.8282711.366525e-011.893701541.2654924114.8322940.417645
4660323.83333360374.530492301.95954337.62961710.895082285.50255643.2246660.000000e+001.636080277.0532204015.9431730.460820
50584818.566214584834.674639314.1408166.9785162.516955342.49951253.0747711.068349e-012.854014506.2855363222.7713770.609286
51108179.156168108188.780606501.68713918.7347446.192684477.39177537.6003066.789015e-012.373191414.5260605498.5215030.453391
5260272.18546060297.463282500.51192118.8660057.771088484.25288738.1564367.766051e-012.432707603.6088246318.3558860.424681
53170722.063220170681.473226450.23256712.7497014.771019423.18484732.1369425.991110e-012.437072324.0653905674.2022880.625288
5485306.53355085318.399332481.58820319.6829706.922146469.43521429.5432686.425049e-012.497703203.0262444077.1020580.610594
566913.7803036930.661764430.93894629.0190334.721459421.72388833.2857832.302183e-013.217713163.1479852297.2236260.439487
5857577.55131357589.476710401.17143613.9431263.460870379.10694122.4765023.255794e-012.574096300.1780323818.7477230.498902
5931940.60065431947.858909291.25538412.9094881.154495322.99480758.0593018.326188e-013.5092230.0000003641.6732700.729460
60197420.717079197321.111906211.6564805.4648380.945457247.68651742.6340541.793349e-015.685922190.4162032229.2947920.597746
61254286.115697254247.490267263.6487544.4665860.794033336.60236072.8876240.000000e+001.938614290.8125892668.1456280.577633
62177872.914216177864.884855284.6529824.2758400.796263362.09200876.8280170.000000e+002.037210437.1456892776.2313940.459794
63191308.330411191208.756162303.2584656.4664900.471597359.33096363.3861033.051031e-014.285515751.6784452634.5578450.538122
64193934.674654193832.039293319.9568883.2979930.476294377.96957566.6037601.229366e+001.771440223.7762463001.6242910.504488
65268705.528770268635.363922245.7500303.5442800.660387313.96807767.8117460.000000e+002.123849179.1935412502.4817360.518695
66234966.724030234927.323055224.3377423.8046810.789005290.19368865.0764440.000000e+002.139242159.1690722358.3367380.587815
6771483.52924571496.764756363.7392267.7125952.164735388.74088650.5437963.731947e-012.468416264.8417894426.0182380.503898
68170664.045350170593.245968341.1400238.2524842.347105366.26999248.2917444.531270e-012.616498392.2003184290.9070320.627410
69255354.614148255243.634342398.1948929.5274142.944664410.62782745.0412339.403005e-012.721314357.4786824369.9693340.603660
7287339.98662487358.478608377.37259715.8899284.414315374.83292539.4854342.887332e+003.643563452.9866444724.3107720.638322
7466822.78473966830.910878352.34653916.1324432.831254356.06281237.9604702.574042e-012.699090475.8320094006.9348070.667023
7690999.92063590987.484131312.44171617.1974753.243708327.14616261.3250584.594797e-013.78851910.3108635095.3750770.563549
8253742.15032553724.244194300.1339211.1251990.485408375.24363473.3900823.570894e-011.031886120.4714322354.5445080.401188
84114379.547619114312.167712165.8547653.7713760.630626214.14074739.6325541.147322e-0111.56239280.5781241575.5776140.598063
86109150.480303109099.040451251.0809032.9641000.037449313.32726964.4163560.000000e+004.026967264.1752471848.5243870.549545
8730310.22500030279.747522201.8604854.5125160.240302195.96263219.6966573.365469e-011.94711330.7228402081.3873380.533753
89124524.436126124485.234078303.8268127.6672451.191275324.08410637.6321113.197981e-018.81284562.7717053863.1545900.586516
205299681.378783299592.707962217.9299479.8356709.330200221.53583154.9395392.151090e+002.90439816.8420712926.6096180.650941
206238975.674216238868.787783271.6594804.7215980.697541351.86221479.6985292.754881e-012.09872897.3173592606.6944870.589749
207530627.898633530496.553495388.07819015.8495123.781185445.04698479.4014890.000000e+002.592672119.3689043676.1516280.596090
208133550.241026133552.619235329.0237999.8537622.490395326.70580031.5806022.837028e-012.004973109.4198253008.2798500.591694
500165112.148907165100.949397234.18121120.7333202.202552290.42377882.1378420.000000e+005.0986814.1779354284.5194840.641251
\n", "
" ], "text/plain": [ " ridership_per_stop_left pred_ridership_per_stop_left whitePop_left \\\n", "RT \n", "2 134522.944788 134534.933482 208.267392 \n", "4 99808.540597 99805.952204 294.027808 \n", "5 39859.112121 39831.697560 300.474488 \n", "6 70198.159740 70151.561310 323.780105 \n", "7 172704.875000 172705.516626 272.825121 \n", "8 31700.785109 31715.974935 345.976548 \n", "10 180693.320513 180688.863148 293.626300 \n", "11 106647.419048 106647.802261 181.120285 \n", "12 85971.939683 85869.967130 217.193877 \n", "13 352065.832937 351993.357497 303.795568 \n", "14 622898.507567 622629.520302 260.635485 \n", "15 480979.640229 480916.501601 218.530810 \n", "16 61913.339683 61832.045438 173.252459 \n", "17 69928.573016 69832.930591 260.891003 \n", "19 92025.039683 91947.301004 278.905230 \n", "20 18662.667063 18666.881909 219.472950 \n", "21 37845.840747 37849.810601 333.356175 \n", "24 122421.171047 122415.645083 332.246318 \n", "25 85211.193939 85213.359826 248.000134 \n", "26 75149.279895 75170.468097 230.363305 \n", "32 89786.133117 89819.274260 391.892723 \n", "33 139203.941558 139245.216131 330.384085 \n", "34 233473.055575 233521.007256 348.550397 \n", "35 301469.718615 301429.061166 432.892573 \n", "36 223034.837876 223033.080681 341.594838 \n", "37 405965.933333 405853.282068 281.590568 \n", "41 100968.400000 100992.533399 307.232031 \n", "43 146544.766667 146450.246174 282.834047 \n", "44 191258.266667 191192.397839 351.666212 \n", "46 60323.833333 60374.530492 301.959543 \n", "50 584818.566214 584834.674639 314.140816 \n", "51 108179.156168 108188.780606 501.687139 \n", "52 60272.185460 60297.463282 500.511921 \n", "53 170722.063220 170681.473226 450.232567 \n", "54 85306.533550 85318.399332 481.588203 \n", "56 6913.780303 6930.661764 430.938946 \n", "58 57577.551313 57589.476710 401.171436 \n", "59 31940.600654 31947.858909 291.255384 \n", "60 197420.717079 197321.111906 211.656480 \n", "61 254286.115697 254247.490267 263.648754 \n", "62 177872.914216 177864.884855 284.652982 \n", "63 191308.330411 191208.756162 303.258465 \n", "64 193934.674654 193832.039293 319.956888 \n", "65 268705.528770 268635.363922 245.750030 \n", "66 234966.724030 234927.323055 224.337742 \n", "67 71483.529245 71496.764756 363.739226 \n", "68 170664.045350 170593.245968 341.140023 \n", "69 255354.614148 255243.634342 398.194892 \n", "72 87339.986624 87358.478608 377.372597 \n", "74 66822.784739 66830.910878 352.346539 \n", "76 90999.920635 90987.484131 312.441716 \n", "82 53742.150325 53724.244194 300.133921 \n", "84 114379.547619 114312.167712 165.854765 \n", "86 109150.480303 109099.040451 251.080903 \n", "87 30310.225000 30279.747522 201.860485 \n", "89 124524.436126 124485.234078 303.826812 \n", "205 299681.378783 299592.707962 217.929947 \n", "206 238975.674216 238868.787783 271.659480 \n", "207 530627.898633 530496.553495 388.078190 \n", "208 133550.241026 133552.619235 329.023799 \n", "500 165112.148907 165100.949397 234.181211 \n", "\n", " blackPop_left asianPop_left hlPop_left otherRacePop_left \\\n", "RT \n", "2 9.887799 2.213135 309.054605 97.922292 \n", "4 17.776914 2.035270 391.922281 102.258638 \n", "5 11.754595 3.375640 313.013460 26.172151 \n", "6 11.268005 3.375640 347.711036 42.267087 \n", "7 16.937637 3.704804 264.809078 35.694635 \n", "8 16.826645 1.665794 346.276938 42.570599 \n", "10 26.512654 2.220482 338.464838 86.256646 \n", "11 7.650461 8.498604 170.615978 38.439002 \n", "12 2.914674 3.969830 246.261411 55.440696 \n", "13 6.193875 12.329514 279.310287 63.363140 \n", "14 9.297009 8.992134 256.000005 61.420634 \n", "15 8.360215 8.935973 218.787842 53.806072 \n", "16 1.477653 2.870003 157.177239 23.922623 \n", "17 3.844743 9.490762 267.070524 56.813415 \n", "19 4.532799 11.618772 262.231997 53.849798 \n", "20 4.280203 9.281274 231.397089 58.100654 \n", "21 1.840153 1.113253 378.659996 64.390557 \n", "24 5.706959 0.497646 411.517699 77.420557 \n", "25 2.703231 0.746362 273.513526 37.572968 \n", "26 3.597488 2.273301 320.572720 84.137631 \n", "32 10.954668 3.338142 418.893187 50.212330 \n", "33 4.914969 1.920133 349.624606 43.265217 \n", "34 13.615040 4.468771 396.171870 73.100073 \n", "35 11.140815 3.939210 459.834850 57.564989 \n", "36 13.890318 3.574373 375.280980 58.407137 \n", "37 17.835954 6.148458 315.299166 59.950050 \n", "41 15.689165 5.209317 333.195980 48.113433 \n", "43 23.579636 7.161005 294.625179 57.838204 \n", "44 42.591637 7.176825 348.848159 51.828271 \n", "46 37.629617 10.895082 285.502556 43.224666 \n", "50 6.978516 2.516955 342.499512 53.074771 \n", "51 18.734744 6.192684 477.391775 37.600306 \n", "52 18.866005 7.771088 484.252887 38.156436 \n", "53 12.749701 4.771019 423.184847 32.136942 \n", "54 19.682970 6.922146 469.435214 29.543268 \n", "56 29.019033 4.721459 421.723888 33.285783 \n", "58 13.943126 3.460870 379.106941 22.476502 \n", "59 12.909488 1.154495 322.994807 58.059301 \n", "60 5.464838 0.945457 247.686517 42.634054 \n", "61 4.466586 0.794033 336.602360 72.887624 \n", "62 4.275840 0.796263 362.092008 76.828017 \n", "63 6.466490 0.471597 359.330963 63.386103 \n", "64 3.297993 0.476294 377.969575 66.603760 \n", "65 3.544280 0.660387 313.968077 67.811746 \n", "66 3.804681 0.789005 290.193688 65.076444 \n", "67 7.712595 2.164735 388.740886 50.543796 \n", "68 8.252484 2.347105 366.269992 48.291744 \n", "69 9.527414 2.944664 410.627827 45.041233 \n", "72 15.889928 4.414315 374.832925 39.485434 \n", "74 16.132443 2.831254 356.062812 37.960470 \n", "76 17.197475 3.243708 327.146162 61.325058 \n", "82 1.125199 0.485408 375.243634 73.390082 \n", "84 3.771376 0.630626 214.140747 39.632554 \n", "86 2.964100 0.037449 313.327269 64.416356 \n", "87 4.512516 0.240302 195.962632 19.696657 \n", "89 7.667245 1.191275 324.084106 37.632111 \n", "205 9.835670 9.330200 221.535831 54.939539 \n", "206 4.721598 0.697541 351.862214 79.698529 \n", "207 15.849512 3.781185 445.046984 79.401489 \n", "208 9.853762 2.490395 326.705800 31.580602 \n", "500 20.733320 2.202552 290.423778 82.137842 \n", "\n", " nhPop_left aiPop_left disability_left medHHInc_left \\\n", "RT \n", "2 0.000000e+00 3.221515 32.503298 2358.714255 \n", "4 0.000000e+00 4.392465 39.850378 3427.174551 \n", "5 2.725746e-01 7.295883 14.683008 2764.141157 \n", "6 6.911012e-01 1.863522 18.819553 2533.982711 \n", "7 2.042944e-01 1.552696 447.844155 2364.803431 \n", "8 3.759443e-01 3.047165 356.473438 4101.151177 \n", "10 0.000000e+00 4.658511 0.945881 4039.459861 \n", "11 0.000000e+00 1.280814 1.630691 3272.177597 \n", "12 4.049652e-01 0.811182 127.086499 3001.717148 \n", "13 6.113203e+00 1.846071 222.135478 3202.533721 \n", "14 1.733508e+00 1.573087 184.408919 3837.076096 \n", "15 2.901344e+00 2.268997 78.477025 2929.195847 \n", "16 5.762041e-01 0.839681 69.443286 3180.696030 \n", "17 8.388609e-01 0.764677 54.377882 3760.066077 \n", "19 1.771287e+00 0.696525 62.902654 4319.978697 \n", "20 3.531956e-01 1.179460 37.719113 2729.706579 \n", "21 4.420221e-02 1.224643 273.700006 3125.074943 \n", "24 0.000000e+00 2.410588 248.116826 3082.695337 \n", "25 2.290862e-01 1.245279 156.374937 2474.481847 \n", "26 0.000000e+00 0.998893 27.590168 2350.389283 \n", "32 4.634834e-07 1.410105 248.587744 3713.123084 \n", "33 1.664783e-01 0.704070 357.749929 3100.087203 \n", "34 0.000000e+00 4.031467 176.733030 3974.680069 \n", "35 9.304611e-02 2.673003 383.505733 4821.666297 \n", "36 0.000000e+00 2.991883 345.213472 3497.843724 \n", "37 0.000000e+00 1.671791 228.081755 2734.914991 \n", "41 0.000000e+00 1.188075 330.633750 2889.642805 \n", "43 4.695201e-04 2.407509 145.937095 2935.823932 \n", "44 1.366525e-01 1.893701 541.265492 4114.832294 \n", "46 0.000000e+00 1.636080 277.053220 4015.943173 \n", "50 1.068349e-01 2.854014 506.285536 3222.771377 \n", "51 6.789015e-01 2.373191 414.526060 5498.521503 \n", "52 7.766051e-01 2.432707 603.608824 6318.355886 \n", "53 5.991110e-01 2.437072 324.065390 5674.202288 \n", "54 6.425049e-01 2.497703 203.026244 4077.102058 \n", "56 2.302183e-01 3.217713 163.147985 2297.223626 \n", "58 3.255794e-01 2.574096 300.178032 3818.747723 \n", "59 8.326188e-01 3.509223 0.000000 3641.673270 \n", "60 1.793349e-01 5.685922 190.416203 2229.294792 \n", "61 0.000000e+00 1.938614 290.812589 2668.145628 \n", "62 0.000000e+00 2.037210 437.145689 2776.231394 \n", "63 3.051031e-01 4.285515 751.678445 2634.557845 \n", "64 1.229366e+00 1.771440 223.776246 3001.624291 \n", "65 0.000000e+00 2.123849 179.193541 2502.481736 \n", "66 0.000000e+00 2.139242 159.169072 2358.336738 \n", "67 3.731947e-01 2.468416 264.841789 4426.018238 \n", "68 4.531270e-01 2.616498 392.200318 4290.907032 \n", "69 9.403005e-01 2.721314 357.478682 4369.969334 \n", "72 2.887332e+00 3.643563 452.986644 4724.310772 \n", "74 2.574042e-01 2.699090 475.832009 4006.934807 \n", "76 4.594797e-01 3.788519 10.310863 5095.375077 \n", "82 3.570894e-01 1.031886 120.471432 2354.544508 \n", "84 1.147322e-01 11.562392 80.578124 1575.577614 \n", "86 0.000000e+00 4.026967 264.175247 1848.524387 \n", "87 3.365469e-01 1.947113 30.722840 2081.387338 \n", "89 3.197981e-01 8.812845 62.771705 3863.154590 \n", "205 2.151090e+00 2.904398 16.842071 2926.609618 \n", "206 2.754881e-01 2.098728 97.317359 2606.694487 \n", "207 0.000000e+00 2.592672 119.368904 3676.151628 \n", "208 2.837028e-01 2.004973 109.419825 3008.279850 \n", "500 0.000000e+00 5.098681 4.177935 4284.519484 \n", "\n", " employmentHHMix_left \n", "RT \n", "2 0.678890 \n", "4 0.605265 \n", "5 0.627341 \n", "6 0.637560 \n", "7 0.613350 \n", "8 0.672532 \n", "10 0.377877 \n", "11 0.555266 \n", "12 0.637096 \n", "13 0.680011 \n", "14 0.598012 \n", "15 0.702311 \n", "16 0.527821 \n", "17 0.720910 \n", "19 0.639357 \n", "20 0.842817 \n", "21 0.502860 \n", "24 0.430235 \n", "25 0.676294 \n", "26 0.626042 \n", "32 0.354143 \n", "33 0.557372 \n", "34 0.511730 \n", "35 0.560738 \n", "36 0.492910 \n", "37 0.426441 \n", "41 0.318899 \n", "43 0.390286 \n", "44 0.417645 \n", "46 0.460820 \n", "50 0.609286 \n", "51 0.453391 \n", "52 0.424681 \n", "53 0.625288 \n", "54 0.610594 \n", "56 0.439487 \n", "58 0.498902 \n", "59 0.729460 \n", "60 0.597746 \n", "61 0.577633 \n", "62 0.459794 \n", "63 0.538122 \n", "64 0.504488 \n", "65 0.518695 \n", "66 0.587815 \n", "67 0.503898 \n", "68 0.627410 \n", "69 0.603660 \n", "72 0.638322 \n", "74 0.667023 \n", "76 0.563549 \n", "82 0.401188 \n", "84 0.598063 \n", "86 0.549545 \n", "87 0.533753 \n", "89 0.586516 \n", "205 0.650941 \n", "206 0.589749 \n", "207 0.596090 \n", "208 0.591694 \n", "500 0.641251 " ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "routes_to_hex = gpd.overlay(transit_stops, final_hex, how='intersection')\n", "\n", "# Plot the route\n", "fig, ax = plt.subplots(figsize=(10,10))\n", "routes_to_hex.plot(ax=ax, color='red', markersize=1)\n", "plt.show()\n", "\n", "# Get the hexagon IDs for every hexagon that intersects with the route, and only include the left side of the join\n", "joined = gpd.sjoin(final_hex, routes_to_hex, how='inner', predicate='intersects')\n", "joined.head()\n", "\n", "# Select only the columns we want\n", "joined = joined[['RT', 'ridership_per_stop_left', 'pred_ridership_per_stop_left', 'whitePop_left', 'blackPop_left', 'asianPop_left', 'hlPop_left', 'otherRacePop_left', 'nhPop_left', 'aiPop_left', 'disability_left', 'medHHInc_left', 'employmentHHMix_left']]\n", "joined\n", "\n", "# Aggregation of the data\n", "joined = joined.groupby('RT').agg({'ridership_per_stop_left': 'sum', 'pred_ridership_per_stop_left': 'sum', 'whitePop_left': 'mean', 'blackPop_left': 'mean', 'asianPop_left': 'mean', 'hlPop_left': 'mean', 'otherRacePop_left': 'mean', 'nhPop_left': 'mean', 'aiPop_left': 'mean', 'disability_left': 'sum', 'medHHInc_left': 'mean', 'employmentHHMix_left': 'mean'})\n", "joined.head(61)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "# Export to json\n", "# joined.to_json('C:/Users/jtrum/pennmusa/MUSA8010/repository/data/ridership_joined.json', orient='index')" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RTTPlongitudelatitudeavg_onsavg_offsgeometry
021707 Myrtle\\Williams-106.47034731.7686410.2617190.496094POINT (-106.47035 31.76864)
121900 Magoffin\\Laurel-106.46748131.7680370.3607841.752941POINT (-106.46748 31.76804)
221931 Myrtle\\Eucalyptus-106.46791631.7702293.0195312.636719POINT (-106.46792 31.77023)
322023 Myrtle\\Willow-106.46669031.7709231.3085941.039062POINT (-106.46669 31.77092)
422114 Magoffin\\Walnut-106.46438731.7700180.1294120.074510POINT (-106.46439 31.77002)
52222 Campbell\\First-106.48313431.7567941.0000001.078125POINT (-106.48313 31.75679)
622302 Magoffin\\Palm-106.46252931.7712042.8627457.133333POINT (-106.46253 31.77120)
72Cotton\\Bassett-106.47365931.7653812.6093752.425781POINT (-106.47366 31.76538)
82Downtown Transit Ctr Bay B-106.48945231.75314976.76744249.550388POINT (-106.48945 31.75315)
92Five Points Terminal Bay F-106.46258231.78177541.76953136.171875POINT (-106.46258 31.78178)
102Magoffin\\Cotton-106.47319731.7645004.7421883.878906POINT (-106.47320 31.76450)
112Magoffin\\Dallas-106.46979231.7665460.2431370.682353POINT (-106.46979 31.76655)
122Magoffin\\Eucalyptus-106.46622431.7688356.2078438.976471POINT (-106.46622 31.76884)
132Magoffin\\Lee St.-106.47097331.7657991.5607845.176471POINT (-106.47097 31.76580)
142Magoffin\\Ochoa-106.48135031.7602290.4687500.304688POINT (-106.48135 31.76023)
152Myrtle\\Dallas-106.47154331.7678640.1406250.476562POINT (-106.47154 31.76786)
162Myrtle\\Florence-106.48348631.7601740.4648442.687500POINT (-106.48349 31.76017)
172Myrtle\\Palm-106.46427031.7724821.3164061.609375POINT (-106.46427 31.77248)
182Paisano\\Stanton-106.48546431.7558331.4126988.357143POINT (-106.48546 31.75583)
192Piedras\\Alameda-106.46167131.7738002.3593754.378906POINT (-106.46167 31.77380)
202Piedras\\Alameda-106.46145931.7730172.3593754.378906POINT (-106.46146 31.77302)
212Piedras\\Gateway West-106.46156931.7791301.3710940.496094POINT (-106.46157 31.77913)
222Raynor\\Missouri-106.45989031.7794350.4784312.443137POINT (-106.45989 31.77944)
232San Antonio Transit Terminal-106.48245431.7595226.4804691.320312POINT (-106.48245 31.75952)
242San Antonio\\Cotton-106.47400831.7623693.6601560.902344POINT (-106.47401 31.76237)
252San Antonio\\Park-106.47630531.76121629.36965020.463035POINT (-106.47630 31.76122)
262San Antonio\\Park-106.47613031.76147629.36965020.463035POINT (-106.47613 31.76148)
272San Antonio\\St.Vrain-106.47943731.7599830.4023441.140625POINT (-106.47944 31.75998)
282San Antonio\\Tays-106.47783531.7606882.6835941.058594POINT (-106.47783 31.76069)
292Virgina\\Magoffin-106.48065331.7605300.2695311.183594POINT (-106.48065 31.76053)
\n", "
" ], "text/plain": [ " RT TP longitude latitude avg_ons \\\n", "0 2 1707 Myrtle\\Williams -106.470347 31.768641 0.261719 \n", "1 2 1900 Magoffin\\Laurel -106.467481 31.768037 0.360784 \n", "2 2 1931 Myrtle\\Eucalyptus -106.467916 31.770229 3.019531 \n", "3 2 2023 Myrtle\\Willow -106.466690 31.770923 1.308594 \n", "4 2 2114 Magoffin\\Walnut -106.464387 31.770018 0.129412 \n", "5 2 222 Campbell\\First -106.483134 31.756794 1.000000 \n", "6 2 2302 Magoffin\\Palm -106.462529 31.771204 2.862745 \n", "7 2 Cotton\\Bassett -106.473659 31.765381 2.609375 \n", "8 2 Downtown Transit Ctr Bay B -106.489452 31.753149 76.767442 \n", "9 2 Five Points Terminal Bay F -106.462582 31.781775 41.769531 \n", "10 2 Magoffin\\Cotton -106.473197 31.764500 4.742188 \n", "11 2 Magoffin\\Dallas -106.469792 31.766546 0.243137 \n", "12 2 Magoffin\\Eucalyptus -106.466224 31.768835 6.207843 \n", "13 2 Magoffin\\Lee St. -106.470973 31.765799 1.560784 \n", "14 2 Magoffin\\Ochoa -106.481350 31.760229 0.468750 \n", "15 2 Myrtle\\Dallas -106.471543 31.767864 0.140625 \n", "16 2 Myrtle\\Florence -106.483486 31.760174 0.464844 \n", "17 2 Myrtle\\Palm -106.464270 31.772482 1.316406 \n", "18 2 Paisano\\Stanton -106.485464 31.755833 1.412698 \n", "19 2 Piedras\\Alameda -106.461671 31.773800 2.359375 \n", "20 2 Piedras\\Alameda -106.461459 31.773017 2.359375 \n", "21 2 Piedras\\Gateway West -106.461569 31.779130 1.371094 \n", "22 2 Raynor\\Missouri -106.459890 31.779435 0.478431 \n", "23 2 San Antonio Transit Terminal -106.482454 31.759522 6.480469 \n", "24 2 San Antonio\\Cotton -106.474008 31.762369 3.660156 \n", "25 2 San Antonio\\Park -106.476305 31.761216 29.369650 \n", "26 2 San Antonio\\Park -106.476130 31.761476 29.369650 \n", "27 2 San Antonio\\St.Vrain -106.479437 31.759983 0.402344 \n", "28 2 San Antonio\\Tays -106.477835 31.760688 2.683594 \n", "29 2 Virgina\\Magoffin -106.480653 31.760530 0.269531 \n", "\n", " avg_offs geometry \n", "0 0.496094 POINT (-106.47035 31.76864) \n", "1 1.752941 POINT (-106.46748 31.76804) \n", "2 2.636719 POINT (-106.46792 31.77023) \n", "3 1.039062 POINT (-106.46669 31.77092) \n", "4 0.074510 POINT (-106.46439 31.77002) \n", "5 1.078125 POINT (-106.48313 31.75679) \n", "6 7.133333 POINT (-106.46253 31.77120) \n", "7 2.425781 POINT (-106.47366 31.76538) \n", "8 49.550388 POINT (-106.48945 31.75315) \n", "9 36.171875 POINT (-106.46258 31.78178) \n", "10 3.878906 POINT (-106.47320 31.76450) \n", "11 0.682353 POINT (-106.46979 31.76655) \n", "12 8.976471 POINT (-106.46622 31.76884) \n", "13 5.176471 POINT (-106.47097 31.76580) \n", "14 0.304688 POINT (-106.48135 31.76023) \n", "15 0.476562 POINT (-106.47154 31.76786) \n", "16 2.687500 POINT (-106.48349 31.76017) \n", "17 1.609375 POINT (-106.46427 31.77248) \n", "18 8.357143 POINT (-106.48546 31.75583) \n", "19 4.378906 POINT (-106.46167 31.77380) \n", "20 4.378906 POINT (-106.46146 31.77302) \n", "21 0.496094 POINT (-106.46157 31.77913) \n", "22 2.443137 POINT (-106.45989 31.77944) \n", "23 1.320312 POINT (-106.48245 31.75952) \n", "24 0.902344 POINT (-106.47401 31.76237) \n", "25 20.463035 POINT (-106.47630 31.76122) \n", "26 20.463035 POINT (-106.47613 31.76148) \n", "27 1.140625 POINT (-106.47944 31.75998) \n", "28 1.058594 POINT (-106.47783 31.76069) \n", "29 1.183594 POINT (-106.48065 31.76053) " ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# # Plot one route\n", "# route2 = transit_stops[transit_stops['RT'] == 2]\n", "# route2\n", "\n", "# # Plot the route\n", "# fig, ax = plt.subplots(figsize=(10,10))\n", "# final_hex.plot(ax=ax, color='grey', edgecolor='white')\n", "# route2.plot(ax=ax, color='red', markersize=1)\n", "# plt.show()\n", "\n", "# route2_hex = gpd.overlay(route2, final_hex, how='intersection')\n", "# route2_hex\n", "\n", "# # Plot the route\n", "# fig, ax = plt.subplots(figsize=(10,10))\n", "# route2_hex.plot(ax=ax, color='red', markersize=1)\n", "# plt.show()\n", "\n", "# # Get the hexagon IDs for every hexagon that intersects with the route, and only include the left side of the join\n", "# joined = gpd.sjoin(final_hex, route2_hex, how='inner', predicate='intersects')\n", "# joined.head()\n", "\n", "# # Select only the columns we want\n", "# joined = joined[['RT', 'ridership_per_stop_left', 'pred_ridership_per_stop_left', 'whitePop_left', 'blackPop_left', 'asianPop_left', 'hlPop_left', 'otherRacePop_left', 'nhPop_left', 'aiPop_left', 'disability_left', 'medHHInc_left', 'employmentHHMix_left']]\n", "# joined\n", "\n", "# # Aggregation of the data\n", "# joined = joined.groupby('RT').agg({'ridership_per_stop_left': 'sum', 'pred_ridership_per_stop_left': 'sum', 'whitePop_left': 'mean', 'blackPop_left': 'mean', 'asianPop_left': 'mean', 'hlPop_left': 'mean', 'otherRacePop_left': 'mean', 'nhPop_left': 'mean', 'aiPop_left': 'mean', 'disability_left': 'sum', 'medHHInc_left': 'mean', 'employmentHHMix_left': 'mean'})\n", "# joined" ] } ], "metadata": { "kernelspec": { "display_name": "musa-550-fall-2022", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }