Melbourne Housing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction of Housing Prices for the City of Melbourne"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contents of the demo:\n",
    "* **Uploading CSV** with spatial data (housing sales) to HANA Cloud\n",
    "* Training a **regression model** for the housing prices **without using spatial features**\n",
    "* Retrieve & Upload OpenStreetMap **Points of Interest** for the respective area\n",
    "* **Compute spatial features** by using database functions\n",
    "* Train the same **regression model including spatial features**\n",
    "* **Compare** both models\n",
    "* **Predict the price of a reference house** for different locations across the city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter notebook is almost self-contained. You only need the following to get started:\n",
    "* Vanilla instance of HANA Cloud or HANA2 SPS05 (i.e. HANA Express)\n",
    "* Python dependencies installed\n",
    "* CSV of dataset downloaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dealing with datasets in Python\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "# db connectivity\n",
    "import sqlalchemy\n",
    "\n",
    "# load osm data\n",
    "import osmnx as ox\n",
    "\n",
    "# visualization of spatial data\n",
    "import folium\n",
    "from folium import plugins\n",
    "from shapely import wkt\n",
    "\n",
    "# Python ML\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import median_absolute_error\n",
    "import shap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setup Database Connection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For HANA Cloud we need to used an encrypted connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdb_host = 'xxx'\n",
    "hdb_port = 443\n",
    "hdb_user = 'xxx'\n",
    "hdb_password = 'xxx'\n",
    "\n",
    "connection_string = 'hana://%s:%s@%s:%s/?encrypt=true&sslvalidatecertificate=false' % (hdb_user, hdb_password, hdb_host, hdb_port)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Connect to DB. We configure SqlMagic to automatically return Pandas DataFrames instead of SQL ResultSets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext sql\n",
    "%config SqlMagic.displaylimit = 100\n",
    "%config SqlMagic.autopandas = True\n",
    "%config SqlMagic.displaycon = False\n",
    "%sql $connection_string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Clean up demo**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql DROP TABLE MELB_HOUSING\n",
    "%sql DROP TABLE MELB_OSM_POI\n",
    "%sql DROP TABLE MELB_AREA_OF_INTEREST\n",
    "%sql DROP VIEW MELB_HOUSING_CLOSEST_POI \n",
    "%sql DROP VIEW MELB_HOUSING_COUNT_POI\n",
    "%sql DROP VIEW MELB_HOUSING_PREDICT_VIEW\n",
    "%sql DROP VIEW MELB_HOUSING_PREDICT_GEO_VIEW\n",
    "%sql DROP VIEW MELB_OSM_POI_VIEW\n",
    "%sql DROP VIEW MELB_REGION_PROXY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Determine SRS ID to be used for the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GDA94 / MGA zone 55 http://epsg.io/28355"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " srid = 28355"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the SRS in case it is not yet existing on the instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_srs_exists = %sql SELECT * FROM ST_SPATIAL_REFERENCE_SYSTEMS WHERE SRS_ID = $srid\n",
    "\n",
    "if len(df_srs_exists) == 0:\n",
    "    %sql CREATE PREDEFINED SPATIAL REFERENCE SYSTEM IDENTIFIED BY $srid\n",
    "    print('SRS ' + str(srid) + ' installed')\n",
    "else:\n",
    "    print('SRS ' + str(srid) + ' already existed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper functions to quickly generate maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert pandas dataframe with wkt column to geojson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df2geojson(df_input, geom_col_name):\n",
    "    df_temp = df_input.copy()\n",
    "    df_temp[geom_col_name] = df_temp[geom_col_name].apply(wkt.loads)\n",
    "    gdf_result = gpd.GeoDataFrame(df_temp, geometry = geom_col_name)\n",
    "    return gdf_result.to_json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bring pandas dataframe with wkt column on a Folium map.  \n",
    "https://python-visualization.github.io/folium/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df2map(df_input, geom_col_name, location, zoom_start,style_function=None,tiles='OpenStreetMap'):\n",
    "    m = folium.Map(location=location,zoom_start=zoom_start,tiles=tiles)    \n",
    "    tooltip_fields = list(df_input.loc[:, df_input.columns != geom_col_name].columns)\n",
    "    folium.GeoJson(\n",
    "        df2geojson(df_input[df_input[geom_col_name].notnull()], geom_col_name), \n",
    "        tooltip=folium.features.GeoJsonTooltip(fields=tooltip_fields,labels=True, toLocaleString=True),\n",
    "        style_function=style_function\n",
    "    ).add_to(m)\n",
    "    return m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate a heatmap with Folium's heatmap plugin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df2heatmap(df_input, lat_col_name, lon_col_name, location, zoom_start):\n",
    "    m = folium.Map(location=location, zoom_start=zoom_start)\n",
    "    arr_input = df_input[[lat_col_name,lon_col_name]].to_numpy()\n",
    "    m.add_child(plugins.HeatMap(arr_input, radius=15))\n",
    "    return m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper functions for handling ML models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train a regression model with xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(x_train, y_train):\n",
    "    model = xgb.XGBRegressor(objective='reg:squarederror')\n",
    "    model.fit(x_train, y_train)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine MAE based on sample prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mae_predict(model, x, y):\n",
    "    y_pred = model.predict(x)\n",
    "    mae = median_absolute_error(y, y_pred)\n",
    "    return mae"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually explain the model with shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def explain_model(model, x):\n",
    "    explainer = shap.TreeExplainer(model)\n",
    "    shap_values = explainer.shap_values(x)\n",
    "    shap.summary_plot(shap_values, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload CSV File with Housing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download data from https://www.kaggle.com/anthonypino/melbourne-housing-market/data?select=Melbourne_housing_FULL.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_import = pd.read_csv('../data/Melbourne_housing_FULL.csv')\n",
    "df_import.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add ID field, format Date and adjust column naming (upper case and no spaces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_import['ID'] = df_import.index\n",
    "df_import.columns = df_import.columns.str.strip().str.replace(' ', '_').str.upper()\n",
    "df_import['DATE'] = pd.to_datetime(df_import['DATE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a proof point, drop all fields with a spatial reference - except for lat and lon.  \n",
    "Later on, we would like to show how to generate even more insight by only using lat/lon and advanced spatial data processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_import['SUBURB']\n",
    "del df_import['ADDRESS']\n",
    "del df_import['DISTANCE']\n",
    "del df_import['POSTCODE']\n",
    "del df_import['COUNCILAREA']\n",
    "del df_import['REGIONNAME']\n",
    "del df_import['PROPERTYCOUNT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the data that we are going to upload to HANA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_import.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create table and do the actual upload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "hdb_connection = sqlalchemy.create_engine(connection_string).connect()\n",
    "obj_cols = df_import.select_dtypes(include=[object]).columns.values.tolist()\n",
    "df_import.to_sql(name = 'melb_housing', con = hdb_connection, if_exists = 'replace', chunksize = 500, dtype={c: sqlalchemy.types.String(512) for c in obj_cols})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate geometries from LONGTITUDE and LATTITUDE (sic!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql ALTER TABLE MELB_HOUSING ADD (SHAPE ST_GEOMETRY($srid))\n",
    "%sql UPDATE MELB_HOUSING SET SHAPE = ST_GEOMFROMTEXT('Point('||LONGTITUDE||' '||LATTITUDE||')', 4326).ST_TRANSFORM($srid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create view which filters out houses without location information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql \n",
    "CREATE OR REPLACE VIEW MELB_HOUSING_PREDICT_VIEW AS\n",
    "(\n",
    "    SELECT \n",
    "        CAST(ID AS INTEGER) ID,\n",
    "        CAST(TYPE AS NVARCHAR(1)) TYPE,\n",
    "        SHAPE,\n",
    "        CAST(ROOMS AS INTEGER) ROOMS,\n",
    "        CAST(BEDROOM2 AS INTEGER) BEDROOM2,\n",
    "        CAST(BATHROOM AS INTEGER) BATHROOM,\n",
    "        CAST(CAR AS INTEGER) CAR,\n",
    "        CAST(LANDSIZE AS INTEGER) LANDSIZE,\n",
    "        CAST(BUILDINGAREA AS INTEGER) BUILDINGAREA,\n",
    "        CAST(YEARBUILT AS INTEGER) YEARBUILT,\n",
    "        CAST(PRICE AS DOUBLE) PRICE,\n",
    "        YEAR(DATE) AS YEAR,\n",
    "        ST_GEOMFROMTEXT('POINT EMPTY').ST_ASWKT() AS SHAPE_CELL_WKT -- a hack that will save hassle later..\n",
    "    FROM MELB_HOUSING \n",
    "    WHERE PRICE IS NOT NULL AND SHAPE IS NOT NULL\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check records per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql\n",
    "SELECT year, count(*) as count\n",
    "FROM MELB_HOUSING_PREDICT_VIEW \n",
    "GROUP BY year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use 2016 and 2017 for training and 2018 as a test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a Regression Model for Housing Prices (without spatial features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve data for training and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%sql df_housing << SELECT * FROM MELB_HOUSING_PREDICT_VIEW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate numerical features for xgboost. Note that there are **only non-geo-spatial features** describing the house itself, but not the location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_housing = pd.get_dummies(df_housing[[\n",
    "    'rooms',\n",
    "    'type',\n",
    "    'bedroom2',\n",
    "    'bathroom',\n",
    "    'car',\n",
    "    'landsize',\n",
    "    'buildingarea',\n",
    "    'yearbuilt'\n",
    "]])\n",
    "y_housing = df_housing[['price']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the data of 2016 and 2017 to predict housing prices in 2018. In our example we are going to ignore the influence of time on the housing price, since we want to focus on the spatial aspect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_housing[df_housing[\"year\"] <= 2017]\n",
    "y_train = y_housing[df_housing[\"year\"] <= 2017]\n",
    "x_test = x_housing[df_housing[\"year\"] == 2018]\n",
    "y_test = y_housing[df_housing[\"year\"] == 2018]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = train_model(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the mean absolute error on training and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mae_train_nogeo = mae_predict(model,x_train,y_train)\n",
    "print(str(round(mae_train_nogeo,2)) + ' AUD - mae on training data')\n",
    "mae_test_nogeo = mae_predict(model,x_test,y_test)\n",
    "print(str(round(mae_test_nogeo,2)) + ' AUD - mae on test data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually explain the model  \n",
    "* red color corresponds to a high feature value\n",
    "* blue color corresponds to a low feature value\n",
    "* the x axis depicts the negative or positive influence on the price\n",
    "\n",
    "Example *yearbuilt*: According to the data, old houses tend to be expensive. This could be related to the fact, that old house are typically located in long-established and thus central regions. New houses are often located in development areas in the suburbs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explain_model(model, x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Possible values for field Type:\n",
    "* br - bedroom(s)\n",
    "* h - house,cottage,villa, semi,terrace\n",
    "* u - unit, duplex\n",
    "* t - townhouse\n",
    "* dev site - development site\n",
    "* o res - other residential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve and Upload OSM POI Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand the context of a location, we need to incorporate additional data. OSM Point of Interest data is suitable, since this data helps to understand the surrounding of a house. How far is the next school? Is it a nightlife area? etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use ST_ConvexHullAggr to determine the relevant area to load POIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_poi_shape = %sql SELECT ST_CONVEXHULLAGGR(SHAPE).ST_TRANSFORM(4326).ST_ASWKT() AS SHAPE FROM MELB_HOUSING_PREDICT_VIEW\n",
    "covered_area = df_poi_shape[df_poi_shape.columns[0]].apply(wkt.loads).iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the area that is covered by our housing data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2map(df_poi_shape, 'shape', location=[-37.817301, 144.9665769], zoom_start=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-usable function for downloading POI data and getting the datatypes straight for upload to HANA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pois_df(area, tag_filter, field_list):\n",
    "    gdf_tmp = ox.pois_from_polygon(area, tag_filter)\n",
    "    df_tmp = pd.DataFrame(gdf_tmp)\n",
    "    df_tmp = df_tmp[field_list]\n",
    "    df_tmp[\"geometry\"] = df_tmp[\"geometry\"].astype(\"str\")\n",
    "    df_tmp = df_tmp.infer_objects()\n",
    "    return df_tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the POIs (i.e. Amenities, Shops and Airports) within this area using OSMnx  \n",
    "https://github.com/gboeing/osmnx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "df_poi = get_pois_df(\n",
    "    covered_area,\n",
    "    {'amenity':True,'shop':True,'aeroway':True},\n",
    "    ['osmid','geometry', 'amenity','shop','aeroway','name']\n",
    ")\n",
    "df_poi.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the data that we are going to upload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_poi.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the actual upload of POIs to HANA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "hdb_connection = sqlalchemy.create_engine(connection_string).connect()\n",
    "obj_cols = df_poi.select_dtypes(include=[object]).columns.values.tolist()\n",
    "obj_cols.remove('geometry')\n",
    "df_poi.to_sql(name = 'melb_osm_poi', con = hdb_connection, if_exists = 'replace', chunksize = 1000, dtype={c:sqlalchemy.types.String(512) for c in obj_cols})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql ALTER TABLE MELB_OSM_POI ADD (SHAPE ST_GEOMETRY($srid))\n",
    "%sql UPDATE MELB_OSM_POI SET SHAPE = ST_GEOMFROMTEXT(GEOMETRY, 4326).ST_TRANSFORM($srid)\n",
    "%sql ALTER TABLE MELB_OSM_POI DROP (GEOMETRY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computation of Geo-spatial Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to prepare a few views, that we can later use to compute geo-spatial features for model training and application on the fly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regions and more specifically neighbourhoods are important to housing prices. One way to bring this into our model would be to include the name of the neighbourhood. The more data-driven way, which is independent of local names, is to introduce a cluster as a proxy for the regions. In this example we will devide the area into 15 clusters, which are the Voronoi cells of the k-Means centroids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql\n",
    "CREATE OR REPLACE VIEW MELB_REGION_PROXY AS\n",
    "(\n",
    "    SELECT\n",
    "        ST_ClusterID() AS CLUSTER_ID,\n",
    "        'REGION-' || ST_ClusterID() AS CLUSTER_NAME,\n",
    "        ST_ClusterCell() AS CLUSTER_CELL\n",
    "    FROM MELB_HOUSING_PREDICT_VIEW\n",
    "    GROUP CLUSTER BY SHAPE USING KMEANS CLUSTERS 30\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the cluster that have been identified based on the listings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_regions = %sql SELECT cluster_id, cluster_cell.st_transform(4326).st_aswkt() as shape FROM MELB_REGION_PROXY\n",
    "df2map(df_regions, 'shape', location=[-37.817301, 144.9665769], zoom_start=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a view that adds a few analytical fields to the osm data. First and foremost, we will make sure that each poi is represented by a point (*SHAPE_PT*) rather than a polygon. This is required, because a few of them are represented by polygons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql\n",
    "CREATE OR REPLACE VIEW MELB_OSM_POI_VIEW AS \n",
    "(\n",
    "    SELECT *\n",
    "    FROM \n",
    "    (\n",
    "        SELECT \n",
    "            OSMID, \n",
    "            AMENITY,\n",
    "            NAME,\n",
    "            SHAPE,\n",
    "            CASE WHEN SHAPE.ST_GEOMETRYTYPE() = 'ST_Point' THEN SHAPE ELSE SHAPE.ST_CENTROID() END AS SHAPE_PT,\n",
    "            CASE\n",
    "                WHEN AMENITY in ('bar','restaurant','cafe','fast_food','pub','bbq','bar','theatre','cinema','nightclub') THEN 'entertainment'\n",
    "                WHEN AMENITY in ('pharmacy','clinic','doctors','dentist','hospital','veterinary') THEN 'health'\n",
    "                WHEN AMENITY in ('school','library','college','university') THEN 'education'\n",
    "                WHEN AMENITY in ('kindergarten','childcare') THEN 'childcare'\n",
    "                WHEN AMENITY in ('community_centre','community_hall') THEN 'community'\n",
    "                WHEN AMENITY in ('fire_station','police','hospital') THEN 'emergency'\n",
    "                WHEN SHOP in ('convenience','supermarket') THEN 'grocery'\n",
    "                WHEN SHOP in ('clothes','mall','furniture','jewelry','electronics','books') THEN 'shopping'\n",
    "                WHEN AEROWAY in ('aerodrome','helipad') THEN 'airport'\n",
    "                ELSE 'other'\n",
    "            END AS POI_TYPE\n",
    "        FROM MELB_OSM_POI\n",
    "    )\n",
    "    WHERE POI_TYPE <> 'other'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a heatmap of entertainment POIs to check that everything is working.  \n",
    "(*Try to zoom in to see CBD, Carlton and Fitzroy*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql df_entertainment << \n",
    "SELECT \n",
    "    SHAPE_PT.ST_TRANSFORM(4326).ST_X() as lon,\n",
    "    SHAPE_PT.ST_TRANSFORM(4326).ST_Y() as lat\n",
    "FROM MELB_OSM_POI_VIEW \n",
    "WHERE POI_TYPE = 'entertainment' AND SHAPE_PT IS NOT NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2heatmap(df_entertainment, 'lat', 'lon', location=[-37.817301, 144.9665769], zoom_start=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a parametrized view to determine the closest amenity of a certain type to the individual houses. We use Voronoi tesselation to boost performance and readability of the statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql\n",
    "CREATE OR REPLACE VIEW MELB_HOUSING_CLOSEST_POI (IN POI_TYPE VARCHAR(100)) AS\n",
    "(\n",
    "    SELECT A.ID, A.SHAPE.ST_DISTANCE(B.SHAPE_PT) AS DIST_POI\n",
    "    FROM MELB_HOUSING_PREDICT_VIEW A\n",
    "    LEFT JOIN\n",
    "    (\n",
    "        SELECT OSMID, SHAPE_PT, ST_VoronoiCell(SHAPE_PT, 10.0) OVER () AS VCELL\n",
    "        FROM MELB_OSM_POI_VIEW\n",
    "        WHERE POI_TYPE = \\:POI_TYPE\n",
    "    ) B ON A.SHAPE.ST_INTERSECTS(B.VCELL) = 1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a parametrized view to determine the number of POIs of a given type and within a certain range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%sql\n",
    "CREATE OR REPLACE VIEW MELB_HOUSING_COUNT_POI(IN POI_TYPE VARCHAR(100), IN DISTANCE_METER INTEGER) AS\n",
    "(\n",
    "    SELECT A.ID, COUNT(*) AS CNT_POI\n",
    "    FROM MELB_HOUSING_PREDICT_VIEW A\n",
    "    JOIN MELB_OSM_POI_VIEW B ON A.SHAPE.ST_WITHINDISTANCE(B.SHAPE,\\:DISTANCE_METER) = 1\n",
    "    WHERE \\:POI_TYPE IS NULL OR B.POI_TYPE = \\:POI_TYPE\n",
    "    GROUP BY A.ID\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of calculating the distance to the center of the city (i.e. the town hall), we calculate the distance to the central business district (as a polygon). This way the distance for all houses in the polygon will be 0 and for others the value will better reflect the centrality of the housing as opposed to calculating the distance to an arbitrary point in cbd."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approximate polygon for CBD and Bay (can e.g. be drawn with https://arthur-e.github.io/Wicket/sandbox-gmaps3.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbd_poly = \"POLYGON((144.9560499704408 -37.80598074335628,144.9714136636781 -37.80770998156641,144.97497563724988 -37.81547410106952,144.95999818211072 -37.81964401162185,144.955363324933 -37.80947308471637,144.9560499704408 -37.80598074335628))\"\n",
    "bay_poly = \"MULTILINESTRING ((16139350.388793945 -4566440.82434082, 16139319.78704834 -4566519.42565918, 16139230.063537598 -4566483.518127441, 16139244.39038086 -4566449.176818848, 16139318.774047852 -4566443.307495117, 16139350.388793945 -4566440.82434082), (16113009.737121582 -4626977.554443359, 16113013.633300781 -4626978.7607421875, 16113015.069274902 -4626980.563110352, 16113017.663024902 -4626983.117614746, 16113017.930236816 -4627006.321411133, 16113017.974731445 -4627009.670715332, 16113023.585266113 -4627009.684875488, 16113022.060180664 -4627016.965332031, 16113020.501708984 -4627023.294921875, 16113019.010009766 -4627030.22064209, 16113014.757629395 -4627039.615722656, 16113009.603515625 -4627056.433166504, 16113006.853942871 -4627063.344665527, 16113000.653442383 -4627070.511657715, 16112960.73425293 -4627106.601989746, 16112948.667236328 -4627116.607421875, 16112933.472106934 -4627123.973083496, 16112923.119384766 -4627127.166320801, 16112910.651611328 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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coastline above is actually copied from OSM with a rather high granularity. We use ST_Simplify to reduce the number of points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql CREATE TABLE MELB_AREA_OF_INTEREST (NAME VARCHAR(100), SHAPE ST_GEOMETRY($srid))\n",
    "%sql INSERT INTO MELB_AREA_OF_INTEREST VALUES ('CBD', ST_GeomFromText(:cbd_poly, 4326).ST_Transform(:srid))\n",
    "%sql INSERT INTO MELB_AREA_OF_INTEREST VALUES ('BAY', ST_GeomFromText(:bay_poly, 3857).ST_Simplify(100).ST_Transform(:srid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_si_areas = %sql SELECT NAME, SHAPE.ST_TRANSFORM(4326).ST_ASWKT() AS SHAPE FROM MELB_AREA_OF_INTEREST\n",
    "df2map(df_si_areas, 'shape', location=[-37.817301, 144.9665769], zoom_start=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a Regression Model for Housing Prices (including geo-spatial features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve dataset containing the following types of features:\n",
    "* Features describing the house (H.*). Those features have also been used by the other model\n",
    "* Distance to areas of interest (CBD and Bay). Note that we compute the distance to the polygon rather than to a point\n",
    "* Distance to the closest POI of different types\n",
    "* Number of POIs of different types within a given range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql\n",
    "CREATE OR REPLACE VIEW MELB_HOUSING_PREDICT_GEO_VIEW AS \n",
    "(\n",
    "    SELECT \n",
    "        H.*, \n",
    "        \n",
    "        FLOOR(H.SHAPE.ST_DISTANCE(CBD.SHAPE)/250)*250 AS DIST_CBD,\n",
    "        FLOOR(H.SHAPE.ST_DISTANCE(BAY.SHAPE)/250)*250 AS DIST_BAY,\n",
    "\n",
    "        R.CLUSTER_NAME AS REGION,\n",
    "\n",
    "        poi1.DIST_POI AS DIST_ENTERTAINMENT, \n",
    "        poi2.DIST_POI AS DIST_HEALTH,\n",
    "        poi3.DIST_POI AS DIST_EDUCATION,\n",
    "        poi4.DIST_POI AS DIST_CHILDCARE,\n",
    "        poi6.DIST_POI AS DIST_COMMUNITY,\n",
    "        poi7.DIST_POI AS DIST_EMERGENCY,\n",
    "        poi8.DIST_POI AS DIST_GROCERY,\n",
    "        poi9.DIST_POI AS DIST_SHOPPING,\n",
    "        poi10.DIST_POI AS DIST_AIRPORT,\n",
    "\n",
    "        IFNULL(cnt10.CNT_POI,0) AS CNT_AIRPORT,\n",
    "        IFNULL(cnt0.CNT_POI,0) AS CNT_POI,\n",
    "        IFNULL(cnt1.CNT_POI,0) AS CNT_ENTERTAINMENT,\n",
    "        IFNULL(cnt2.CNT_POI,0) AS CNT_HEALTH,\n",
    "        IFNULL(cnt3.CNT_POI,0) AS CNT_EDUCATION,\n",
    "        IFNULL(cnt4.CNT_POI,0) AS CNT_CHILDCARE,\n",
    "        IFNULL(cnt6.CNT_POI,0) AS CNT_COMMUNITY,\n",
    "        IFNULL(cnt7.CNT_POI,0) AS CNT_EMERGENCY,\n",
    "        IFNULL(cnt8.CNT_POI,0) AS CNT_GROCERY,\n",
    "        IFNULL(cnt9.CNT_POI,0) AS CNT_SHOPPING\n",
    "\n",
    "    FROM MELB_HOUSING_PREDICT_VIEW H\n",
    "\n",
    "    LEFT JOIN MELB_AREA_OF_INTEREST CBD ON CBD.NAME = 'CBD'\n",
    "    LEFT JOIN MELB_AREA_OF_INTEREST BAY ON BAY.NAME = 'BAY'\n",
    "\n",
    "    LEFT JOIN MELB_REGION_PROXY R ON H.SHAPE.ST_INTERSECTS(R.CLUSTER_CELL) = 1\n",
    "\n",
    "    LEFT JOIN MELB_HOUSING_CLOSEST_POI('entertainment') poi1 ON H.ID = poi1.ID\n",
    "    LEFT JOIN MELB_HOUSING_CLOSEST_POI('health') poi2 ON H.ID = poi2.ID\n",
    "    LEFT JOIN MELB_HOUSING_CLOSEST_POI('education') poi3 ON H.ID = poi3.ID\n",
    "    LEFT JOIN MELB_HOUSING_CLOSEST_POI('childcare') poi4 ON H.ID = poi4.ID\n",
    "    LEFT JOIN MELB_HOUSING_CLOSEST_POI('community') poi6 ON H.ID = poi6.ID\n",
    "    LEFT JOIN MELB_HOUSING_CLOSEST_POI('emergency') poi7 ON H.ID = poi7.ID\n",
    "    LEFT JOIN MELB_HOUSING_CLOSEST_POI('grocery') poi8 ON H.ID = poi8.ID\n",
    "    LEFT JOIN MELB_HOUSING_CLOSEST_POI('shopping') poi9 ON H.ID = poi9.ID\n",
    "    LEFT JOIN MELB_HOUSING_CLOSEST_POI('airport') poi10 ON H.ID = poi10.ID\n",
    "\n",
    "    LEFT JOIN MELB_HOUSING_COUNT_POI(null, 1000) cnt0 ON H.ID = cnt0.ID\n",
    "    LEFT JOIN MELB_HOUSING_COUNT_POI('entertainment', 1000) cnt1 ON H.ID = cnt1.ID\n",
    "    LEFT JOIN MELB_HOUSING_COUNT_POI('health', 1000) cnt2 ON H.ID = cnt2.ID\n",
    "    LEFT JOIN MELB_HOUSING_COUNT_POI('education', 1000) cnt3 ON H.ID = cnt3.ID\n",
    "    LEFT JOIN MELB_HOUSING_COUNT_POI('childcare', 1000) cnt4 ON H.ID = cnt4.ID\n",
    "    LEFT JOIN MELB_HOUSING_COUNT_POI('community', 1000) cnt6 ON H.ID = cnt6.ID\n",
    "    LEFT JOIN MELB_HOUSING_COUNT_POI('emergency', 1000) cnt7 ON H.ID = cnt7.ID\n",
    "    LEFT JOIN MELB_HOUSING_COUNT_POI('grocery', 1000) cnt8 ON H.ID = cnt8.ID\n",
    "    LEFT JOIN MELB_HOUSING_COUNT_POI('shopping', 1000) cnt9 ON H.ID = cnt9.ID\n",
    "    LEFT JOIN MELB_HOUSING_COUNT_POI('airport', 1000) cnt10 ON H.ID = cnt10.ID\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "df_housing_geo = %sql SELECT * FROM MELB_HOUSING_PREDICT_GEO_VIEW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate numerical features for xgboost. Note that this time we include **geo-spatial features** describing the location of the house."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_housing_geo = pd.get_dummies(\n",
    "    df_housing_geo[[\n",
    "        'rooms',\n",
    "        'bedroom2',\n",
    "        'bathroom',\n",
    "        'car',\n",
    "        'landsize',\n",
    "        'buildingarea',\n",
    "        'yearbuilt',\n",
    "        'dist_cbd',\n",
    "        'dist_bay',\n",
    "        'region',\n",
    "        'cnt_poi',\n",
    "        'dist_entertainment',\n",
    "        'cnt_entertainment',\n",
    "        'dist_health',\n",
    "        'cnt_health',\n",
    "        'dist_education',\n",
    "        'cnt_education',\n",
    "        'dist_childcare',\n",
    "        'cnt_childcare',\n",
    "        'dist_community',\n",
    "        'cnt_community',\n",
    "        'dist_emergency',\n",
    "        'cnt_emergency',\n",
    "        'dist_grocery',\n",
    "        'cnt_grocery',\n",
    "        'dist_shopping',\n",
    "        'cnt_shopping',\n",
    "        'dist_airport',\n",
    "        'cnt_airport',\n",
    "        'type'\n",
    "]])\n",
    "y_housing_geo = df_housing_geo[['price']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, use the same training and test split as before. We want to ensure maximum comparability between both models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_geo = x_housing_geo[df_housing_geo[\"year\"] <= 2017]\n",
    "y_train_geo = y_housing_geo[df_housing_geo[\"year\"] <= 2017]\n",
    "x_test_geo = x_housing_geo[df_housing_geo[\"year\"] == 2018]\n",
    "y_test_geo = y_housing_geo[df_housing_geo[\"year\"] == 2018]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_geo = train_model(x_train_geo, y_train_geo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the mean absolute error on training and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mae_train_geo = mae_predict(model_geo,x_train_geo,y_train_geo)\n",
    "print('MAE on train: ' + str(mae_train_geo))\n",
    "mae_test_geo = mae_predict(model_geo,x_test_geo,y_test_geo)\n",
    "print('MAE on test: ' + str(mae_test_geo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually explain the model to understand the impact of geo-spatial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explain_model(model_geo, x_train_geo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comparison of models with and without geo dimension**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine the median housing price to evaluate the quality of prediction using mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_med_price = %sql SELECT MEDIAN(PRICE) AS p FROM MELB_HOUSING_PREDICT_VIEW\n",
    "median_price = df_med_price['p'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare quality of prediction between non-spatial and spatial model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(str(round(mae_test_nogeo,2)) + ' AUD - mae on test data without spatial features')\n",
    "print(str(round(mae_test_geo,2)) + ' AUD - mae on test data with spatial features')\n",
    "\n",
    "print('===')\n",
    "err_ratio_nogeo = 100 * mae_test_nogeo / median_price\n",
    "err_ratio_geo = 100 * mae_test_geo / median_price\n",
    "print(str(round(err_ratio_nogeo,2)) + '% - error ratio without spatial features')\n",
    "print(str(round(err_ratio_geo,2)) + '% - error ratio with spatial features')\n",
    "\n",
    "print('===')\n",
    "print(str(round(err_ratio_nogeo - err_ratio_geo,2)) + '% - decrease in error ratio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare robustness of prediction between non-spatial and spatial model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mae_dev_nogeo = abs(100*(1-mae_train_nogeo/mae_test_nogeo))\n",
    "print(str(round(mae_dev_nogeo,2)) + '% - MAE Deviation Train/Test without spatial features')\n",
    "\n",
    "mae_dev_geo = abs(100*(1-mae_train_geo/mae_test_geo))\n",
    "print(str(round(mae_dev_geo,2)) + '% - MAE Deviation Train/Test with spatial features')\n",
    "\n",
    "print('===')\n",
    "print(str(round(mae_dev_nogeo - mae_dev_geo,2)) + '% - increase in prediction robustness')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prediction with reference house**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we predicted the price of a house based on the attributes of the house as well as the geo location. To isolate the impact of the geo dimension, we will now predict a \"standard\" reference house for different locations in the city. We are spanning an equidistant (hexagonal) grid and predict for each grid cell's centroid. The below feature values are more or less the average values from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql\n",
    "CREATE OR REPLACE VIEW MELB_HOUSING_PREDICT_VIEW AS\n",
    "(\n",
    "    SELECT\n",
    "        ST_ClusterId() AS ID,\n",
    "        3 AS ROOMS,\n",
    "        'h' as TYPE,\n",
    "        3 as BEDROOM2,\n",
    "        2 as BATHROOM,\n",
    "        2 as CAR,\n",
    "        600 as LANDSIZE,\n",
    "        160 as BUILDINGAREA,\n",
    "        1965 as YEARBUILT,\n",
    "        ST_ClusterCell().ST_Centroid() AS SHAPE,\n",
    "        ST_ClusterCell().ST_Transform(4326).ST_ASWKT() AS SHAPE_CELL_WKT,\n",
    "        2020 as YEAR,\n",
    "        CAST(0 as DOUBLE) as PRICE    \n",
    "    FROM MELB_HOUSING\n",
    "    GROUP CLUSTER BY SHAPE USING HEXAGON X CELLS 75\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare the geo-spatial features similar to above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "df_housing_ref_geo = %sql SELECT * FROM MELB_HOUSING_PREDICT_GEO_VIEW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_housing_ref_geo = pd.get_dummies(\n",
    "    df_housing_ref_geo[[\n",
    "        'rooms',\n",
    "        'bedroom2',\n",
    "        'bathroom',\n",
    "        'car',\n",
    "        'landsize',\n",
    "        'buildingarea',\n",
    "        'yearbuilt',\n",
    "        'dist_cbd',\n",
    "        'dist_bay',\n",
    "        'region',\n",
    "        'cnt_poi',\n",
    "        'dist_entertainment',\n",
    "        'cnt_entertainment',\n",
    "        'dist_health',\n",
    "        'cnt_health',\n",
    "        'dist_education',\n",
    "        'cnt_education',\n",
    "        'dist_childcare',\n",
    "        'cnt_childcare',\n",
    "        'dist_community',\n",
    "        'cnt_community',\n",
    "        'dist_emergency',\n",
    "        'cnt_emergency',\n",
    "        'dist_grocery',\n",
    "        'cnt_grocery',\n",
    "        'dist_shopping',\n",
    "        'cnt_shopping',\n",
    "        'dist_airport',\n",
    "        'cnt_airport',\n",
    "        'type'\n",
    "]])\n",
    "x_housing_ref_geo['type_t'] = 0\n",
    "x_housing_ref_geo['type_u'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict prices for our standard house and different locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_housing_ref_geo['price'] = model_geo.predict(x_housing_ref_geo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine cell color based on predicted price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_housing_ref_vis = df_housing_ref_geo.copy()[[\n",
    "    'id','price','shape_cell_wkt',\n",
    "    'dist_cbd','dist_bay','region',\n",
    "    'cnt_poi','dist_health','dist_community'\n",
    "]]\n",
    "\n",
    "\n",
    "min_price = df_housing_ref_vis['price'].min()\n",
    "print('minimum predicted price: ' + str(min_price) + ' AUD')\n",
    "max_price = df_housing_ref_vis['price'].max()\n",
    "print('maximum predicted price: ' + str(max_price) + ' AUD')\n",
    "\n",
    "def price_to_rgb(price):\n",
    "    ratio = (price-min_price)/(max_price-min_price)\n",
    "    margin = 0.1\n",
    "    ratio = min(1,max(0,ratio-margin)/(1-2*margin))\n",
    "    return '#%02x%02x%02x' % (int((ratio**(1/float(5)))*255), int((1-ratio)*255), int((1-ratio)*255))\n",
    "\n",
    "df_housing_ref_vis['color'] = df_housing_ref_vis['price'].apply(price_to_rgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df2map(\n",
    "    df_housing_ref_vis, \n",
    "    'shape_cell_wkt', \n",
    "    location=[-37.817301, 144.9665769], \n",
    "    zoom_start=10,\n",
    "    style_function = lambda feature: {\n",
    "        'fillColor': feature['properties']['color'],\n",
    "        'color' : feature['properties']['color'],\n",
    "        'weight' : 0.5,\n",
    "        'fillOpacity' : 0.7,\n",
    "    },\n",
    "    tiles='Stamen Toner'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: Cell ID 272 - Frankston East. Far away from CBD, but still 1.9m AUD predicted reference price  \n",
    "https://www.google.com/maps/@-38.1591711,145.1008108,876m/data=!3m1!1e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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