Minggu 2 - Machine Learning untuk Aplikasi Geospasial.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Minggu 2 - Machine Learning untuk Aplikasi Geospasial.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPe1EpKQlgSZZPz6JvDl8yd"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"OlUxH4SW_RSJ"},"source":["# Machine Learning untuk Aplikasi Geospasial\r\n","\r\n","Bagian kedua dari tiga seri Geospatial Data Crash Course  ini akan membahas contoh-contoh aplikasi Machine Learning untuk bidang Geospasial. Pada [bagian pertama](https://colab.research.google.com/drive/1MyjuxE2O-3YX1Q0m6EMA6xrKeum6sETE?usp=sharing) telah dibahas mengenai dasar Geospatial Data Science dan penggunaan Python untuk keperluan pengolahan data spasial. Bagian kedua ini membahas mengenai *machine learning* yang diterapkan untuk beberapa contoh kasus pada bidang Geospasial."]},{"cell_type":"markdown","metadata":{"id":"eQmJiIIvJuQa"},"source":["## Pengantar\r\n","\r\n","Machine learning adalah *'buzzword of the century'*. Beragam aplikasi yang kita saksikan sehari-hari saat ini adalah bagian dari produk yang dihasilkan oleh machine learning. Machine learning merupakan bagian dari bidang Artificial Intelligence yang memberikan komputer kemampuan untuk belajar dari data yang dimasukkan kepada mesin untuk dipelajari. Selanjutnya, mesin dapat menentukan pola dan memuat jawaban dari pertanyaan yang diajukan.\r\n","\r\n","![](https://i.redd.it/f1uzd9twkwb61.jpg)\r\n","\r\n","\r\n","Machine learning saat ini diterapkan pada hampir semua bidang, termasuk pada bidang geospasial. Pada notebook ini akan diberikan contoh mengenai bagaimana machine learning dapat digunakan dalam keperluan analisis spasial, khususnya ketika mengolah data dalam jumlah besar.\r\n","\r\n","\r\n","\r\n"]},{"cell_type":"markdown","metadata":{"id":"phu8LK9AMX7Y"},"source":["## Studi Kasus\r\n","\r\n","Pada bagian ini akan diberikan beberapa Contoh studi kasus untuk dilakukan. Penjelasan untuk tiap kode dapat dijumpai sebagai komentar maupun baris paragraf yang diberikan sebelumnya."]},{"cell_type":"markdown","metadata":{"id":"H-49wxmC-B1U"},"source":["### Teachable Machine \r\n","\r\n","Teachable Machine merupakan antarmuka untuk mesin Machine Learning yang dibuat oleh Google untuk memudahkan pemahaman mengenai cara kerja Machine Learning. Di belakang layar, Teachable Machine menggunakan Tensorflow untuk membuat model dari data yang dimasukkan oleh pengguna. \r\n","\r\n","![](https://scx2.b-cdn.net/gfx/news/2019/teachablemac.gif)\r\n","\r\n","Pada latihan berikut kita akan menggunakan Teachable Machine untuk melatin mesin ini mengenali beberapa kelas data yang berbeda. Prosedurnya cukup mudah:\r\n","\r\n","1. Unduh data untuk latihan klasifikasi. Anda dapat menggunakan data berikut:\r\n","\r\n","> https://drive.google.com/file/d/1fgtwpGIh5lWv0H4FOwmFm_dtdURqSzax/view?usp=sharing\r\n","\r\n","2. Buka website Teachable Machine: https://teachablemachine.withgoogle.com/\r\n","\r\n","3. Buat kelas. Kelas-kelas ini dibuat sesuai dengan jenis hewan, misalnya ayam, sapi, dst.\r\n","\r\n","4. Lakukan training untuk membuat model.\r\n","\r\n","5. Gunakan gambar baru untuk melakukan prediksi. Misalnya gambar seperti berikut:\r\n","\r\n","{}(https://static.dw.com/image/47863948_303.jpg)\r\n","\r\n","Lihat apakah mesin berhasil mendeteksi objek tersebut sebagai seekor sapi.\r\n","\r\n","\r\n","\r\n"]},{"cell_type":"markdown","metadata":{"id":"EljIlSMhtJve"},"source":["### Menggunakan Scikit-Learn\r\n","\r\n","Scikit-Learn merupakan modul Python yang digunakan untuk keperluan pemodelan, analisis statistik serta pembuatan machine learning. Scikit-Learn memiliki modul yang sangat lengkap untuk keperluan machine learning, sekaligus menyediakan kemampuan untuk melakukan modifikasi dan tuning parameter model machine learning yang dikembangkan sendiri.\r\n","\r\n","![](https://scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_001.png)\r\n","\r\n","\r\n","\r\n","Penjelasan mengenai Scikit Learn sendiri dapat dijumpai lebih lengkap pada dokumentasi yang tersedia. Pada contoh di bawah ini, kita akan melakukan klasifikasi sederhana menggunakan classifier MeanShift. Detilnya dapat dilihat pada:\r\n","\r\n","https://scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html"]},{"cell_type":"code","metadata":{"id":"pq4RzdpYtNNW","executionInfo":{"status":"ok","timestamp":1613801792064,"user_tz":-420,"elapsed":1372,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}}},"source":["import numpy as np\r\n","from sklearn.cluster import MeanShift, estimate_bandwidth\r\n","from sklearn.datasets import make_blobs"],"execution_count":65,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KFbB_B2qADS1"},"source":["Pertama, kita akan buat data random sebagai masukan untuk klasifikasi. Data ini dapat dibuat menggunakan fungsi `make_blobs` pada scikit learn, seperti berikut:"]},{"cell_type":"code","metadata":{"id":"Z4fWCMTv__B0","executionInfo":{"status":"ok","timestamp":1613806463196,"user_tz":-420,"elapsed":869,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}}},"source":["# membuat data sampel\r\n","centers = [[1, 1], [-1, -1], [1, -1]]\r\n","X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6)"],"execution_count":109,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2PDwOR0GALu3"},"source":["Pada data di atas dapat kita lihat bahwa kita meminta scikit learn untuk memuat 10000 data dengan standar deviasi 0.6. Kita dapat mengganti data ini dengan data lainnya, misalnya data covid yang kita gunakan di minggu sebelumnya."]},{"cell_type":"markdown","metadata":{"id":"kc7G6dUsAlYj"},"source":["MeanShift merupakan salah satu metode untuk melakukan klasifikasi dari serangkaian data menggunakan metode unsupervised learning. Terlebih dahulu kita perlu membuat bandwith yang menyatakan nilai kuantil dan besaran sampel yang akan digunakan pada tiap klasifikasi, seperti berikut:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D7l0oun2AMcu","executionInfo":{"status":"ok","timestamp":1613806467534,"user_tz":-420,"elapsed":1205,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"068282d0-f380-4c40-d822-a34a73cfa9aa"},"source":["# menghitung clustering dengan MeanShift\r\n","\r\n","# Menghitung Bandwith\r\n","bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)\r\n","\r\n","# unsupervised machine learning\r\n","ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\r\n","ms.fit(X)\r\n","labels = ms.labels_\r\n","cluster_centers = ms.cluster_centers_\r\n","\r\n","labels_unique = np.unique(labels)\r\n","n_clusters_ = len(labels_unique)\r\n","\r\n","print(\"number of estimated clusters : %d\" % n_clusters_)\r\n"],"execution_count":110,"outputs":[{"output_type":"stream","text":["number of estimated clusters : 3\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gRRBqjIv_tNF"},"source":["Selanjutnya, kita dapat mencoba menampilkan hasil klustering yang kita buat. Kita gunakan Matplotlib untuk keperluan plotting dan melihat bagaimana hasil klasifikasi yang dibuat oleh SKLearn."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281},"id":"78l47Ht9_sr7","executionInfo":{"status":"ok","timestamp":1613806484375,"user_tz":-420,"elapsed":1434,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"9521d9ba-563a-4e2b-f128-1cebca46e770"},"source":["import matplotlib.pyplot as plt\r\n","from itertools import cycle\r\n","\r\n","plt.figure(1)\r\n","plt.clf()\r\n","\r\n","colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\r\n","for k, col in zip(range(n_clusters_), colors):\r\n","    my_members = labels == k\r\n","    cluster_center = cluster_centers[k]\r\n","    plt.plot(X[my_members, 0], X[my_members, 1], col + '.')\r\n","    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\r\n","             markeredgecolor='k', markersize=14)\r\n","plt.title('Estimasi jumlah kluster: %d' % n_clusters_)\r\n","plt.show()"],"execution_count":111,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"v42g94iNCXde"},"source":["Dari contoh di atas, kita dapat gunakan model klasifikasi unsupervised ini pada berbagai data lain. Klasifikasi jenis ini banyak digunakan sebagai data masukan pada klasifikasi supervised. "]},{"cell_type":"markdown","metadata":{"id":"pfSTREOgCiD-"},"source":["**Sebagai latihan**, gunakan metode MeanShift di atas untuk membuat klasifikasi dari berbagai data lain yang dapat Anda temukan, misalnya data Covid atau data gempa bumi dari latihan sebelumnya."]},{"cell_type":"code","metadata":{"id":"eKDiyxlNJvkq","executionInfo":{"status":"ok","timestamp":1613801792987,"user_tz":-420,"elapsed":2272,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}}},"source":[""],"execution_count":68,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7W3RqUgXJyzi"},"source":["### Melakukan Analisis Spasial dengan Machine Learning\r\n","\r\n","Pada latihan ini, kita akan lakukan contoh analisis spasial yang sederhana dengan menggunakan Machine Learning, khususnya modul Scikit-Learn pada Python. Kita akan gunakan metode [Ball Tree](https://towardsdatascience.com/tree-algorithms-explained-ball-tree-algorithm-vs-kd-tree-vs-brute-force-9746debcd940) yang merupakan salah satu algoritma Nearest Neighbor untuk analisis titik terdekat."]},{"cell_type":"markdown","metadata":{"id":"ElTY0co2C_YM"},"source":["Pada latihan ini kita akan banyak menggunakan modul Python dengan nama [OSMNX](https://osmnx.readthedocs.io/en/stable/osmnx.html). Modul ini digunakan untuk berinteraksi dengan data OSM agar dapat langsung digunakan dan dianalisis di Python. Untuk itu kita perlu melakukan instalasi modul ini terlebih dahulu."]},{"cell_type":"markdown","metadata":{"id":"OCkLNDt3ZVXT"},"source":["> Analisisnya: Carilah bangunan (rumah) yang paling dekat dengan titik-titik sekolah di Kota Sleman"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t_eShHg1N4FN","executionInfo":{"status":"ok","timestamp":1613801795596,"user_tz":-420,"elapsed":4852,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"e9e31ead-1c4c-460f-f901-2e3c18a83ed7"},"source":["!pip install osmnx"],"execution_count":69,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: osmnx in /usr/local/lib/python3.6/dist-packages (1.0.1)\n","Requirement already satisfied: requests>=2.25 in /usr/local/lib/python3.6/dist-packages (from osmnx) (2.25.1)\n","Requirement already satisfied: pyproj>=2.6 in /usr/local/lib/python3.6/dist-packages (from osmnx) (3.0.0.post1)\n","Requirement already satisfied: pandas>=1.1 in /usr/local/lib/python3.6/dist-packages (from osmnx) (1.1.5)\n","Requirement already satisfied: numpy>=1.19 in /usr/local/lib/python3.6/dist-packages (from osmnx) (1.19.5)\n","Requirement already satisfied: geopandas>=0.8 in /usr/local/lib/python3.6/dist-packages (from osmnx) (0.8.2)\n","Requirement already satisfied: Rtree>=0.9 in /usr/local/lib/python3.6/dist-packages (from osmnx) (0.9.7)\n","Requirement already satisfied: descartes>=1.1 in /usr/local/lib/python3.6/dist-packages (from osmnx) (1.1.0)\n","Requirement already satisfied: Shapely>=1.7 in /usr/local/lib/python3.6/dist-packages (from osmnx) (1.7.1)\n","Requirement already satisfied: networkx>=2.5 in /usr/local/lib/python3.6/dist-packages (from osmnx) (2.5)\n","Requirement already satisfied: matplotlib>=3.3 in /usr/local/lib/python3.6/dist-packages (from osmnx) (3.3.4)\n","Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.25->osmnx) (3.0.4)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.25->osmnx) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.25->osmnx) (2020.12.5)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.25->osmnx) (2.10)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=1.1->osmnx) (2.8.1)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=1.1->osmnx) (2018.9)\n","Requirement already satisfied: fiona in /usr/local/lib/python3.6/dist-packages (from geopandas>=0.8->osmnx) (1.8.18)\n","Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.5->osmnx) (4.4.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.3->osmnx) (0.10.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.3->osmnx) (2.4.7)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.3->osmnx) (7.0.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.3->osmnx) (1.3.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=1.1->osmnx) (1.15.0)\n","Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas>=0.8->osmnx) (0.7.1)\n","Requirement already satisfied: munch in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas>=0.8->osmnx) (2.5.0)\n","Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas>=0.8->osmnx) (20.3.0)\n","Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas>=0.8->osmnx) (1.1.1)\n","Requirement already satisfied: click<8,>=4.0 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas>=0.8->osmnx) (7.1.2)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"XbIXp6FzDQHR"},"source":["Selanjutnya, kita akan gunakan modul ini untuk memanggil titik-titik POI dari OSM ke dalam Google Colab:"]},{"cell_type":"code","metadata":{"id":"oJHue-iFMXOF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1613807328359,"user_tz":-420,"elapsed":852,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"93311a08-ef8b-480a-80e9-0e09b19e10f0"},"source":["# memanggil modul\r\n","import osmnx as ox\r\n","\r\n","all_pois = ox.geometries_from_place(\r\n","            'Sleman, ID',\r\n","            tags = {'amenity':True})\r\n","\r\n","all_pois['amenity'].value_counts()"],"execution_count":112,"outputs":[{"output_type":"execute_result","data":{"text/plain":["place_of_worship      26\n","school                18\n","clinic                12\n","police                 4\n","restaurant             4\n","marketplace            4\n","fuel                   4\n","atm                    3\n","parking_space          3\n","community_centre       2\n","social_facility        2\n","motorcycle_parking     1\n","library                1\n","bank                   1\n","courthouse             1\n","townhall               1\n","kindergarten           1\n","cafe                   1\n","hospital               1\n","ice_cream              1\n","Name: amenity, dtype: int64"]},"metadata":{"tags":[]},"execution_count":112}]},{"cell_type":"markdown","metadata":{"id":"xbJt9MOkDb1t"},"source":["Dari data di atas, kita akan coba panggil titik-titik sekolah:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"IdHVRZB0DbfE","executionInfo":{"status":"ok","timestamp":1613801796134,"user_tz":-420,"elapsed":5377,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"f8eba461-c5d8-4218-ee0f-94aaad91a562"},"source":["# Run query\r\n","sekolah = ox.geometries_from_place(\r\n","            'Sleman, ID',\r\n","            tags = {'amenity':'school'},\r\n","            which_result=1)\r\n","\r\n","sekolah.head()"],"execution_count":71,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>unique_id</th>\n","      <th>osmid</th>\n","      <th>element_type</th>\n","      <th>amenity</th>\n","      <th>name</th>\n","      <th>geometry</th>\n","      <th>religion</th>\n","      <th>nodes</th>\n","      <th>building</th>\n","      <th>building:levels</th>\n","      <th>building:roof</th>\n","      <th>building:structure</th>\n","      <th>building:use</th>\n","      <th>building:walls</th>\n","      <th>wheelchair</th>\n","      <th>school:type_idn</th>\n","      <th>alt_name</th>\n","      <th>isced:level</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>node/1361205677</td>\n","      <td>1361205677</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>MTS N Sleman</td>\n","      <td>POINT (110.34106 -7.71269)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>node/1364438005</td>\n","      <td>1364438005</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMP 2 Sleman</td>\n","      <td>POINT (110.34274 -7.69025)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>node/1364438080</td>\n","      <td>1364438080</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMP Kanisius Sleman</td>\n","      <td>POINT (110.34234 -7.68944)</td>\n","      <td>christian</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>node/2004886528</td>\n","      <td>2004886528</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMK YPKK</td>\n","      <td>POINT (110.34882 -7.70051)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>node/2130483746</td>\n","      <td>2130483746</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>MI Al Ihsan</td>\n","      <td>POINT (110.34016 -7.67447)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         unique_id       osmid  ... alt_name isced:level\n","0  node/1361205677  1361205677  ...      NaN         NaN\n","1  node/1364438005  1364438005  ...      NaN         NaN\n","2  node/1364438080  1364438080  ...      NaN         NaN\n","3  node/2004886528  2004886528  ...      NaN         NaN\n","4  node/2130483746  2130483746  ...      NaN         NaN\n","\n","[5 rows x 18 columns]"]},"metadata":{"tags":[]},"execution_count":71}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ErV_FNxySp2b","executionInfo":{"status":"ok","timestamp":1613801796140,"user_tz":-420,"elapsed":5371,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"63a8f075-86e3-41f1-afaf-e2a744784c34"},"source":["# berapa jumlah sekolah di Sleman?\r\n","sekolah['osmid'].count()"],"execution_count":72,"outputs":[{"output_type":"execute_result","data":{"text/plain":["18"]},"metadata":{"tags":[]},"execution_count":72}]},{"cell_type":"markdown","metadata":{"id":"7mKzxcxYDrUm"},"source":["Demikian pula, kita panggil bangunan. Kali ini kita panggil sebagai poligon:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":462},"id":"tFAkq92GQFQl","executionInfo":{"status":"ok","timestamp":1613807723696,"user_tz":-420,"elapsed":6234,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"deef4180-01b1-46b9-a88b-eef3cbc29883"},"source":["bangunan_gdf = ox.geometries_from_place(\r\n","            'Sleman, ID',\r\n","            tags = {'building':True})\r\n","\r\n","bangunan_gdf.head()"],"execution_count":114,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>unique_id</th>\n","      <th>osmid</th>\n","      <th>element_type</th>\n","      <th>amenity</th>\n","      <th>building</th>\n","      <th>name</th>\n","      <th>opening_hours</th>\n","      <th>religion</th>\n","      <th>geometry</th>\n","      <th>addr:full</th>\n","      <th>capacity</th>\n","      <th>content</th>\n","      <th>man_made</th>\n","      <th>operator</th>\n","      <th>source</th>\n","      <th>nodes</th>\n","      <th>building:levels</th>\n","      <th>building:roof</th>\n","      <th>building:structure</th>\n","      <th>building:use</th>\n","      <th>building:walls</th>\n","      <th>leisure</th>\n","      <th>sport</th>\n","      <th>height</th>\n","      <th>social_facility:for</th>\n","      <th>addr</th>\n","      <th>atm</th>\n","      <th>roof:colour</th>\n","      <th>roof:height</th>\n","      <th>roof:shape</th>\n","      <th>access</th>\n","      <th>covered</th>\n","      <th>surface</th>\n","      <th>denomination</th>\n","      <th>brand</th>\n","      <th>access:roof</th>\n","      <th>building:condition</th>\n","      <th>building:floor</th>\n","      <th>building:material</th>\n","      <th>capacity:persons</th>\n","      <th>alt_name</th>\n","      <th>roof:orientation</th>\n","      <th>healthcare</th>\n","      <th>operator:type</th>\n","      <th>addr:postcode</th>\n","      <th>addr:street</th>\n","      <th>school:type_idn</th>\n","      <th>addr:city</th>\n","      <th>addr:place</th>\n","      <th>addr:province</th>\n","      <th>marketplace:authority</th>\n","      <th>marketplace:type</th>\n","      <th>ways</th>\n","      <th>type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>node/1360237977</td>\n","      <td>1360237977</td>\n","      <td>node</td>\n","      <td>place_of_worship</td>\n","      <td>mosque</td>\n","      <td>Masjid Agung Sleman</td>\n","      <td>24/7</td>\n","      <td>muslim</td>\n","      <td>POINT (110.35530 -7.71438)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>node/1361166353</td>\n","      <td>1361166353</td>\n","      <td>node</td>\n","      <td>place_of_worship</td>\n","      <td>mosque</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>muslim</td>\n","      <td>POINT (110.35599 -7.71841)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>node/2004848178</td>\n","      <td>2004848178</td>\n","      <td>node</td>\n","      <td>NaN</td>\n","      <td>yes</td>\n","      <td>KODIM SLEMAN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (110.33987 -7.68310)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>node/5864963383</td>\n","      <td>5864963383</td>\n","      <td>node</td>\n","      <td>NaN</td>\n","      <td>storage_tank</td>\n","      <td>SPBE PT. Jatirata Mitra Mulya</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (110.33666 -7.67678)</td>\n","      <td>jl raya caturharjo</td>\n","      <td>50 mton</td>\n","      <td>gas</td>\n","      <td>storage_tank</td>\n","      <td>jatirata</td>\n","      <td>survey</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>way/121525425</td>\n","      <td>121525425</td>\n","      <td>way</td>\n","      <td>NaN</td>\n","      <td>yes</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POLYGON ((110.34698 -7.69741, 110.34711 -7.697...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[1360229990, 1360229955, 1360229984, 136022990...</td>\n","      <td>1</td>\n","      <td>tile</td>\n","      <td>reinforced_masonry</td>\n","      <td>place_of_worship</td>\n","      <td>brick</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         unique_id       osmid element_type  ... marketplace:type ways type\n","0  node/1360237977  1360237977         node  ...              NaN  NaN  NaN\n","1  node/1361166353  1361166353         node  ...              NaN  NaN  NaN\n","2  node/2004848178  2004848178         node  ...              NaN  NaN  NaN\n","3  node/5864963383  5864963383         node  ...              NaN  NaN  NaN\n","4    way/121525425   121525425          way  ...              NaN  NaN  NaN\n","\n","[5 rows x 54 columns]"]},"metadata":{"tags":[]},"execution_count":114}]},{"cell_type":"markdown","metadata":{"id":"t5aUJM7oGXXI"},"source":["Berapa jumlahnya?"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H-NMHpnaGZQ9","executionInfo":{"status":"ok","timestamp":1613807728805,"user_tz":-420,"elapsed":868,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"5768feb1-6b3f-4945-8f7b-28811cbdf22b"},"source":["bangunan_gdf['osmid'].count()"],"execution_count":115,"outputs":[{"output_type":"execute_result","data":{"text/plain":["29880"]},"metadata":{"tags":[]},"execution_count":115}]},{"cell_type":"markdown","metadata":{"id":"IHUNKPn7D-QF"},"source":["Terdapat 29.880 bangunan yang kita peroleh. Kita coba tampilkan data tersebut, bagaimana distribusi datanya?"]},{"cell_type":"code","metadata":{"id":"sUI7qQQ7Maut","colab":{"base_uri":"https://localhost:8080/","height":622},"executionInfo":{"status":"ok","timestamp":1613807794977,"user_tz":-420,"elapsed":10249,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"253d3cb2-2379-4df1-8388-2922ffc12f3c"},"source":["# Download London's Boundary\r\n","sleman = ox.geocoder.geocode_to_gdf('Sleman, ID')\r\n","\r\n","# Set up a plot axis\r\n","fig, ax = plt.subplots(figsize = (15,10))\r\n","\r\n","# Visualise both on the plot\r\n","sleman.plot(ax = ax, alpha = 0.5)\r\n","sekolah.plot(ax = ax, markersize = 8, color = 'red', alpha = 0.8, label = 'Lokasi Sekolah')\r\n","bangunan_gdf.plot(ax = ax, markersize = 1, color = 'blue', alpha = 0.8, label = 'Lokasi Bangunan')\r\n","plt.legend()"],"execution_count":116,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7fbc8d9fa400>"]},"metadata":{"tags":[]},"execution_count":116},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"TDWjGY-yEBEB"},"source":["Saatnya melakukan analisis Machine Learning. Fungsi berikut kita gunakan untuk melakukan analisis Nearest Neighbor pada titik bangunan dan sekolah:"]},{"cell_type":"code","metadata":{"id":"endDeT8cPiYN","executionInfo":{"status":"ok","timestamp":1613801987225,"user_tz":-420,"elapsed":1076,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}}},"source":["from sklearn.neighbors import BallTree\r\n","import numpy as np\r\n","\r\n","def get_nearest(src_points, candidates, k_neighbors=1):\r\n","    \"\"\"Menentukan NN dari dua\"\"\"\r\n","\r\n","    # Create tree from the candidate points\r\n","    tree = BallTree(candidates, leaf_size=15, metric='haversine')\r\n","\r\n","    # Find closest points and distances\r\n","    distances, indices = tree.query(src_points, k=k_neighbors)\r\n","\r\n","    # Transpose to get distances and indices into arrays\r\n","    distances = distances.transpose()\r\n","    indices = indices.transpose()\r\n","\r\n","    # Get closest indices and distances (i.e. array at index 0)\r\n","    # note: for the second closest points, you would take index 1, etc.\r\n","    closest = indices[0]\r\n","    closest_dist = distances[0]\r\n","\r\n","    # Return indices and distances\r\n","    return (closest, closest_dist)\r\n","\r\n","\r\n","def nearest_neighbor(left_gdf, right_gdf, return_dist=False):\r\n","    \"\"\"\r\n","    For each point in left_gdf, find closest point in right GeoDataFrame and return them.\r\n","\r\n","    NOTICE: Assumes that the input Points are in WGS84 projection (lat/lon).\r\n","    \"\"\"\r\n","\r\n","    left_geom_col = left_gdf.geometry.name\r\n","    right_geom_col = right_gdf.geometry.name\r\n","\r\n","    # Ensure that index in right gdf is formed of sequential numbers\r\n","    right = right_gdf.copy().reset_index(drop=True)\r\n","\r\n","    # Parse coordinates from points and insert them into a numpy array as RADIANS\r\n","    left_radians = np.array(left_gdf[left_geom_col].apply(lambda geom: (geom.centroid.x * np.pi / 180, geom.centroid.y * np.pi / 180)).to_list())\r\n","    right_radians = np.array(right[right_geom_col].apply(lambda geom: (geom.centroid.x * np.pi / 180, geom.centroid.y * np.pi / 180)).to_list())\r\n","\r\n","    # Find the nearest points\r\n","    # -----------------------\r\n","    # closest ==> index in right_gdf that corresponds to the closest point\r\n","    # dist ==> distance between the nearest neighbors (in meters)\r\n","\r\n","    closest, dist = get_nearest(src_points=left_radians, candidates=right_radians)\r\n","\r\n","    # Return points from right GeoDataFrame that are closest to points in left GeoDataFrame\r\n","    closest_points = right.loc[closest]\r\n","\r\n","    # Ensure that the index corresponds the one in left_gdf\r\n","    closest_points = closest_points.reset_index(drop=True)\r\n","\r\n","    # Add distance if requested\r\n","    # --ini adalah haversine distance--\r\n","    if return_dist:\r\n","        # Convert to meters from radians\r\n","        earth_radius = 6371000  # meters\r\n","        closest_points['distance'] = dist * earth_radius\r\n","\r\n","    return closest_points"],"execution_count":88,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"QgqfYy05EMmK"},"source":["Kita coba tampilkan waktu yang digunakan oleh mesin ini untuk menganalisis menggunakan fungsi di atas. Perintah `%%time` digunakan untuk keperluan ini"]},{"cell_type":"code","metadata":{"id":"YPm8tgcEMy_n","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1613807954965,"user_tz":-420,"elapsed":2221,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"ec4cc584-059f-4222-986d-7fd547289120"},"source":["# mulai analisis. berapa waktunya?\r\n","%%time\r\n","closest_points = nearest_neighbor(bangunan_gdf, sekolah, return_dist=True)"],"execution_count":117,"outputs":[{"output_type":"stream","text":["CPU times: user 1.32 s, sys: 0 ns, total: 1.32 s\n","Wall time: 1.33 s\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"OjJyJwwOHCg2"},"source":["Hasilnya?"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":949},"id":"cMsZtMCMHDvY","executionInfo":{"status":"ok","timestamp":1613802260094,"user_tz":-420,"elapsed":952,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"018e3829-8878-43c6-ddae-fa5ae4576a85"},"source":["# Melihat hasilnya:\r\n","closest_points"],"execution_count":100,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>unique_id</th>\n","      <th>osmid</th>\n","      <th>element_type</th>\n","      <th>amenity</th>\n","      <th>name</th>\n","      <th>geometry</th>\n","      <th>religion</th>\n","      <th>nodes</th>\n","      <th>building</th>\n","      <th>building:levels</th>\n","      <th>building:roof</th>\n","      <th>building:structure</th>\n","      <th>building:use</th>\n","      <th>building:walls</th>\n","      <th>wheelchair</th>\n","      <th>school:type_idn</th>\n","      <th>alt_name</th>\n","      <th>isced:level</th>\n","      <th>distance</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>way/121654650</td>\n","      <td>121654650</td>\n","      <td>way</td>\n","      <td>school</td>\n","      <td>SD N denggung</td>\n","      <td>POLYGON ((110.35825 -7.71752, 110.35824 -7.717...</td>\n","      <td>NaN</td>\n","      <td>[1361179119, 1361179174, 1361179167, 136117917...</td>\n","      <td>yes</td>\n","      <td>1</td>\n","      <td>tile</td>\n","      <td>confined_masonry</td>\n","      <td>education</td>\n","      <td>brick</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>383.380652</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>way/121654650</td>\n","      <td>121654650</td>\n","      <td>way</td>\n","      <td>school</td>\n","      <td>SD N denggung</td>\n","      <td>POLYGON ((110.35825 -7.71752, 110.35824 -7.717...</td>\n","      <td>NaN</td>\n","      <td>[1361179119, 1361179174, 1361179167, 136117917...</td>\n","      <td>yes</td>\n","      <td>1</td>\n","      <td>tile</td>\n","      <td>confined_masonry</td>\n","      <td>education</td>\n","      <td>brick</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>284.443507</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>node/3320084561</td>\n","      <td>3320084561</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMP 1 Sleman</td>\n","      <td>POINT (110.33946 -7.68396)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>56.523568</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>node/3320084661</td>\n","      <td>3320084661</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMA 1 Sleman</td>\n","      <td>POINT (110.33923 -7.68309)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>375.118245</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>node/2004886528</td>\n","      <td>2004886528</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMK YPKK</td>\n","      <td>POINT (110.34882 -7.70051)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>227.037962</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>29875</th>\n","      <td>way/121654650</td>\n","      <td>121654650</td>\n","      <td>way</td>\n","      <td>school</td>\n","      <td>SD N denggung</td>\n","      <td>POLYGON ((110.35825 -7.71752, 110.35824 -7.717...</td>\n","      <td>NaN</td>\n","      <td>[1361179119, 1361179174, 1361179167, 136117917...</td>\n","      <td>yes</td>\n","      <td>1</td>\n","      <td>tile</td>\n","      <td>confined_masonry</td>\n","      <td>education</td>\n","      <td>brick</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>115.939315</td>\n","    </tr>\n","    <tr>\n","      <th>29876</th>\n","      <td>way/121654650</td>\n","      <td>121654650</td>\n","      <td>way</td>\n","      <td>school</td>\n","      <td>SD N denggung</td>\n","      <td>POLYGON ((110.35825 -7.71752, 110.35824 -7.717...</td>\n","      <td>NaN</td>\n","      <td>[1361179119, 1361179174, 1361179167, 136117917...</td>\n","      <td>yes</td>\n","      <td>1</td>\n","      <td>tile</td>\n","      <td>confined_masonry</td>\n","      <td>education</td>\n","      <td>brick</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>110.430668</td>\n","    </tr>\n","    <tr>\n","      <th>29877</th>\n","      <td>node/2004886528</td>\n","      <td>2004886528</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMK YPKK</td>\n","      <td>POINT (110.34882 -7.70051)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>408.570256</td>\n","    </tr>\n","    <tr>\n","      <th>29878</th>\n","      <td>node/2004886528</td>\n","      <td>2004886528</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMK YPKK</td>\n","      <td>POINT (110.34882 -7.70051)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>478.036597</td>\n","    </tr>\n","    <tr>\n","      <th>29879</th>\n","      <td>way/314101461</td>\n","      <td>314101461</td>\n","      <td>way</td>\n","      <td>school</td>\n","      <td>NaN</td>\n","      <td>POLYGON ((110.37240 -7.70208, 110.37289 -7.702...</td>\n","      <td>NaN</td>\n","      <td>[3201411807, 3201411808, 3201411804, 320141180...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>no</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>641.804127</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>29880 rows × 19 columns</p>\n","</div>"],"text/plain":["             unique_id       osmid  ... isced:level    distance\n","0        way/121654650   121654650  ...         NaN  383.380652\n","1        way/121654650   121654650  ...         NaN  284.443507\n","2      node/3320084561  3320084561  ...         NaN   56.523568\n","3      node/3320084661  3320084661  ...         NaN  375.118245\n","4      node/2004886528  2004886528  ...         NaN  227.037962\n","...                ...         ...  ...         ...         ...\n","29875    way/121654650   121654650  ...         NaN  115.939315\n","29876    way/121654650   121654650  ...         NaN  110.430668\n","29877  node/2004886528  2004886528  ...         NaN  408.570256\n","29878  node/2004886528  2004886528  ...         NaN  478.036597\n","29879    way/314101461   314101461  ...         NaN  641.804127\n","\n","[29880 rows x 19 columns]"]},"metadata":{"tags":[]},"execution_count":100}]},{"cell_type":"markdown","metadata":{"id":"_CCQ0cIdEYOS"},"source":["Scroll ke kanan untuk melihat bahwa tiap data telah dihitung jaraknya, antara sekolah dan bangunan di Sleman. Kita bisa cek apakah seluruh titik sudah dihitung:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y5h5E6TzYysV","executionInfo":{"status":"ok","timestamp":1613802001830,"user_tz":-420,"elapsed":880,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"abf6114c-740c-46f4-9368-6214311a5ff3"},"source":["print(len(closest_points), '==', len(bangunan_gdf))"],"execution_count":90,"outputs":[{"output_type":"stream","text":["29880 == 29880\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6Vh81KuBHISw"},"source":["Gabungkan hasil ini menggunakan Left Join. Geopandas dapat kita gunakan untuk keperluan ini:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":513},"id":"Gi96jTVaZONj","executionInfo":{"status":"ok","timestamp":1613802326285,"user_tz":-420,"elapsed":911,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"301aae4b-d874-40f6-9c93-ff0e24b4d8d5"},"source":["# Rename the geometry of closest stops gdf so that we can easily identify it\r\n","closest_points = closest_points.rename(columns={'geom': 'closest_stop_geom'})\r\n","\r\n","# Merge the datasets by index (for this, it is good to use '.join()' -function)\r\n","bangunan_gdf = bangunan_gdf.join(closest_points, how = 'left', lsuffix = '_left', rsuffix = '_right')\r\n","\r\n","# Let's see what we have\r\n","bangunan_gdf.head()"],"execution_count":103,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>unique_id_left</th>\n","      <th>osmid_left</th>\n","      <th>element_type_left</th>\n","      <th>amenity_left</th>\n","      <th>building_left</th>\n","      <th>name_left</th>\n","      <th>opening_hours</th>\n","      <th>religion_left</th>\n","      <th>geometry_left</th>\n","      <th>addr:full</th>\n","      <th>capacity</th>\n","      <th>content</th>\n","      <th>man_made</th>\n","      <th>operator</th>\n","      <th>source</th>\n","      <th>nodes_left</th>\n","      <th>building:levels_left</th>\n","      <th>building:roof_left</th>\n","      <th>building:structure_left</th>\n","      <th>building:use_left</th>\n","      <th>building:walls_left</th>\n","      <th>leisure</th>\n","      <th>sport</th>\n","      <th>height</th>\n","      <th>social_facility:for</th>\n","      <th>addr</th>\n","      <th>atm</th>\n","      <th>roof:colour</th>\n","      <th>roof:height</th>\n","      <th>roof:shape</th>\n","      <th>access</th>\n","      <th>covered</th>\n","      <th>surface</th>\n","      <th>denomination</th>\n","      <th>brand</th>\n","      <th>access:roof</th>\n","      <th>building:condition</th>\n","      <th>building:floor</th>\n","      <th>building:material</th>\n","      <th>capacity:persons</th>\n","      <th>alt_name_left</th>\n","      <th>roof:orientation</th>\n","      <th>healthcare</th>\n","      <th>operator:type</th>\n","      <th>addr:postcode</th>\n","      <th>addr:street</th>\n","      <th>school:type_idn_left</th>\n","      <th>addr:city</th>\n","      <th>addr:place</th>\n","      <th>addr:province</th>\n","      <th>marketplace:authority</th>\n","      <th>marketplace:type</th>\n","      <th>ways</th>\n","      <th>type</th>\n","      <th>unique_id_right</th>\n","      <th>osmid_right</th>\n","      <th>element_type_right</th>\n","      <th>amenity_right</th>\n","      <th>name_right</th>\n","      <th>geometry_right</th>\n","      <th>religion_right</th>\n","      <th>nodes_right</th>\n","      <th>building_right</th>\n","      <th>building:levels_right</th>\n","      <th>building:roof_right</th>\n","      <th>building:structure_right</th>\n","      <th>building:use_right</th>\n","      <th>building:walls_right</th>\n","      <th>wheelchair</th>\n","      <th>school:type_idn_right</th>\n","      <th>alt_name_right</th>\n","      <th>isced:level</th>\n","      <th>distance</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>node/1360237977</td>\n","      <td>1360237977</td>\n","      <td>node</td>\n","      <td>place_of_worship</td>\n","      <td>mosque</td>\n","      <td>Masjid Agung Sleman</td>\n","      <td>24/7</td>\n","      <td>muslim</td>\n","      <td>POINT (110.35530 -7.71438)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>way/121654650</td>\n","      <td>121654650</td>\n","      <td>way</td>\n","      <td>school</td>\n","      <td>SD N denggung</td>\n","      <td>POLYGON ((110.35825 -7.71752, 110.35824 -7.717...</td>\n","      <td>NaN</td>\n","      <td>[1361179119, 1361179174, 1361179167, 136117917...</td>\n","      <td>yes</td>\n","      <td>1</td>\n","      <td>tile</td>\n","      <td>confined_masonry</td>\n","      <td>education</td>\n","      <td>brick</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>383.380652</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>node/1361166353</td>\n","      <td>1361166353</td>\n","      <td>node</td>\n","      <td>place_of_worship</td>\n","      <td>mosque</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>muslim</td>\n","      <td>POINT (110.35599 -7.71841)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>way/121654650</td>\n","      <td>121654650</td>\n","      <td>way</td>\n","      <td>school</td>\n","      <td>SD N denggung</td>\n","      <td>POLYGON ((110.35825 -7.71752, 110.35824 -7.717...</td>\n","      <td>NaN</td>\n","      <td>[1361179119, 1361179174, 1361179167, 136117917...</td>\n","      <td>yes</td>\n","      <td>1</td>\n","      <td>tile</td>\n","      <td>confined_masonry</td>\n","      <td>education</td>\n","      <td>brick</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>284.443507</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>node/2004848178</td>\n","      <td>2004848178</td>\n","      <td>node</td>\n","      <td>NaN</td>\n","      <td>yes</td>\n","      <td>KODIM SLEMAN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (110.33987 -7.68310)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>node/3320084561</td>\n","      <td>3320084561</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMP 1 Sleman</td>\n","      <td>POINT (110.33946 -7.68396)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>56.523568</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>node/5864963383</td>\n","      <td>5864963383</td>\n","      <td>node</td>\n","      <td>NaN</td>\n","      <td>storage_tank</td>\n","      <td>SPBE PT. Jatirata Mitra Mulya</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (110.33666 -7.67678)</td>\n","      <td>jl raya caturharjo</td>\n","      <td>50 mton</td>\n","      <td>gas</td>\n","      <td>storage_tank</td>\n","      <td>jatirata</td>\n","      <td>survey</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>node/3320084661</td>\n","      <td>3320084661</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMA 1 Sleman</td>\n","      <td>POINT (110.33923 -7.68309)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>375.118245</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>way/121525425</td>\n","      <td>121525425</td>\n","      <td>way</td>\n","      <td>NaN</td>\n","      <td>yes</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POLYGON ((110.34698 -7.69741, 110.34711 -7.697...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>[1360229990, 1360229955, 1360229984, 136022990...</td>\n","      <td>1</td>\n","      <td>tile</td>\n","      <td>reinforced_masonry</td>\n","      <td>place_of_worship</td>\n","      <td>brick</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>node/2004886528</td>\n","      <td>2004886528</td>\n","      <td>node</td>\n","      <td>school</td>\n","      <td>SMK YPKK</td>\n","      <td>POINT (110.34882 -7.70051)</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>227.037962</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    unique_id_left  osmid_left  ... isced:level    distance\n","0  node/1360237977  1360237977  ...         NaN  383.380652\n","1  node/1361166353  1361166353  ...         NaN  284.443507\n","2  node/2004848178  2004848178  ...         NaN   56.523568\n","3  node/5864963383  5864963383  ...         NaN  375.118245\n","4    way/121525425   121525425  ...         NaN  227.037962\n","\n","[5 rows x 73 columns]"]},"metadata":{"tags":[]},"execution_count":103}]},{"cell_type":"markdown","metadata":{"id":"6Ua6C0oqEvR5"},"source":["Kita juga dapat menerapkan statistik sederhana untuk menghitung berbagai parameter:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"l7Dm2aZVaKyX","executionInfo":{"status":"ok","timestamp":1613802329873,"user_tz":-420,"elapsed":1100,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"c41c6964-8bdb-4889-fd94-62a9b566e2f7"},"source":["bangunan_gdf['distance'].describe()"],"execution_count":104,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    29880.000000\n","mean       437.339847\n","std        226.266271\n","min          0.000000\n","25%        262.302510\n","50%        419.713412\n","75%        586.081842\n","max       1695.880020\n","Name: distance, dtype: float64"]},"metadata":{"tags":[]},"execution_count":104}]},{"cell_type":"markdown","metadata":{"id":"5XiGi33LExqd"},"source":["Terakhir, kita akan gunakan beberapa modul untuk menampilkan klasifikasi data. Mapclassify digunakan untuk keperluan ini, sehingga perlu kita install terlebih dahulu:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FsuQdh3caYFb","executionInfo":{"status":"ok","timestamp":1613801815969,"user_tz":-420,"elapsed":25079,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"67bd5145-4280-4a8a-c2a6-eebf7cabce4a"},"source":["## install mapclassify\r\n","!pip install mapclassify"],"execution_count":81,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: mapclassify in /usr/local/lib/python3.6/dist-packages (2.4.2)\n","Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.6/dist-packages (from mapclassify) (1.4.1)\n","Requirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.6/dist-packages (from mapclassify) (1.1.5)\n","Requirement already satisfied: numpy>=1.3 in /usr/local/lib/python3.6/dist-packages (from mapclassify) (1.19.5)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.6/dist-packages (from mapclassify) (2.5)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from mapclassify) (0.22.2.post1)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=1.0->mapclassify) (2.8.1)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=1.0->mapclassify) (2018.9)\n","Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx->mapclassify) (4.4.2)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->mapclassify) (1.0.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0->mapclassify) (1.15.0)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Ixf7siU8E8Dh"},"source":["Kemudian, kita tampilkan seperti berikut:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":558},"id":"bqSmq9nDaSFR","executionInfo":{"status":"ok","timestamp":1613802365873,"user_tz":-420,"elapsed":5973,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}},"outputId":"91a40c94-c0e6-4199-e5e6-f46259c20446"},"source":["closest_points.plot(column='distance', markersize=0.2, alpha=0.5, figsize=(10,10), scheme='quantiles', k=4,\r\n","               legend=True)"],"execution_count":106,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fbc92a0a8d0>"]},"metadata":{"tags":[]},"execution_count":106},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"ecyI3wXc5wKW"},"source":["Dengan demikian kita telah melakukan analisis untuk melihat bagaimana machine learning dapat kita gunakan untuk melakukan analisis pencarian lokasi dengan lebih mudah dan lebih cepat. "]},{"cell_type":"markdown","metadata":{"id":"Eqf3EYmyFALV"},"source":["**Sebagai Latihan**, lakukan analisis yang sama pada berbagai data yang berbeda: di kota lain dengan jenis 'amenities' OSM yang berbeda. Bagaimana hasilnya?"]},{"cell_type":"markdown","metadata":{"id":"LqjgaoMr8b1T"},"source":["## Rujukan\r\n","\r\n","* https://towardsdatascience.com/exploring-classifiers-with-python-scikit-learn-iris-dataset-2bcb490d2e1b \r\n","* https://medium.com/spatial-data-science/deep-learning-for-geospatial-data-applications-multi-label-classification-2b0a1838fcf3\r\n","* https://automating-gis-processes.github.io/site/notebooks/L3/nearest-neighbor-faster.html\r\n","* https://colab.research.google.com/github/sentinel-hub/eo-learn/blob/master/examples/land-cover-map/SI_LULC_pipeline.ipynb"]},{"cell_type":"code","metadata":{"id":"gDLLpW_Y5TUV","executionInfo":{"status":"aborted","timestamp":1613801815972,"user_tz":-420,"elapsed":25067,"user":{"displayName":"Dany Puguh Laksono","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjd8eOzv4V9R3fnhZHMJEN542UAJIK5XPlKWPS5=s64","userId":"10973890960709691040"}}},"source":[""],"execution_count":null,"outputs":[]}]}