SC02_Gruppe_10_04_report.ipynb

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   "source": [
    "# Import necessary libraries for geospatial data & visualization\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import plotly.express as px"
   ]
  },
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   "cell_type": "markdown",
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    "# Cost of Living in Switzerland over a period of 10 years (2013-2022)\n",
    "![\"data/processed/wordcloud.png\"](./data/processed/wordcloud.png \"Wordcloud of the cheapest (biggest) and most expensive (smallest) cantons in Switzerland\")\n",
    "## Introduction\n",
    "Switzerland is a country known for its high cost of living.\n",
    "\n",
    "We will analyze the cost of living in Switzerland over a period of 10 years (2013-2022) based on the two seemingly most significant positions:\n",
    "- the cost of rent\n",
    "- the cost of health insurance\n",
    "\n",
    "Our analysis is based on data from admin.ch from the departments BAG and BFS.\n",
    "\n",
    "This report is based on the data gathering, cleanup and visualization from the three previous notebooks. **Execuiting the code in this notebook will not work without the data from the previous notebooks.**\n",
    "\n",
    "The project is publicly accessible and the latest stable state can be found in the develop branch of SC02_Group10 repository on [GitHub](https://github.com/pToker/SC02_Gruppe_10/tree/develop)."
   ]
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    "## Table of Contents\n",
    "1. [Data Gathering / Collection](#data-gathering)\n",
    "2. [Data Preparation](#data-preparation)\n",
    "3. [Data Visualization](#data-visualization)\n",
    "4. [Conclusion](#conclusion)\n"
   ]
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    "## Data Gathering / Collection <a class=\"anchor\" id =\"data-gathering\"></a>\n",
    "In the notebook 'SC02_Gruppe_10_01_gather_data.ipynb' we gathered the data from the following sources:\n",
    " - [BFS - Rent Prices](https://dam-api.bfs.admin.ch/hub/api/dam/assets/30885375/master)\n",
    " - [BAG - Health Insurance Prices](https://www.bag.admin.ch/dam/bag/de/dokumente/kuv-aufsicht/stat/publications-aos/stat_kv_22_xlsx.zip.download.zip/STATKV%202022%20XLSX%20v20231207.zip)\n",
    "\n",
    "As we can never be sure how remote sources develop over time we have saved the data in the 'data/original' folder of the repository. The data is saved in the following files:\n",
    " - 'rent_prices.xlsx'\n",
    " - 'health-care_premium.xlsx'\n",
    "\n",
    "(These are the original files, the cleaned files are saved in the 'data/raw' folder)\n",
    "\n",
    "Accessing data from a remote service is error prone wherefore we have attemted to perform some simple error handling in case of a connection error but won't be able to guarantee that the data is still accessible and and all cases are covered.<br/>\n",
    "Please change from log level 'DEBUG' to 'TRACE' in case a data source is not accessible to get more information about the error.<br/>\n",
    "By skipping the 1^st^ and starting directly with the 2^nd^ notebook (SC02_Gruppe_10_02_cleanup_data.ipynb) the data already in the project can be used for further analysis.\n",
    "\n",
    "## Data Cleanup / Preparation <a class=\"anchor\" id=\"data-preparation\"></a>\n",
    "We will analyze the cost of living in Switzerland over a period of 10 years (2013-2022). This period of time has not been selected at will but has come to be due to the availability of data.\n",
    "Due to the fact that the amount of data is limited and fairly easily analyzable by hand we have decided to only perform the steps clearly needed to clean up the data. This includes:\n",
    "- removing unnecessary columns\n",
    "- renaming columns\n",
    "- converting data types (e.g. from string to float)\n",
    "- removing years without data in the other dataset\n",
    "\n",
    "Steps which were not necessary due to the nature of the data:\n",
    "- removing duplicates\n",
    "- removing rows with invalid values\n",
    "- removing rows with missing values\n",
    "\n",
    "For performance reasons we have decided to save the cleaned data in the 'data/raw' folder of the repository. The cleaned data is saved in feather format in separate dataframes per category but also in a combined dataframe (overall_prices.feather).\n",
    "We have only learned about the alternative 'parquet' format after the data cleanup and have decided to stick with feather for the sake of consistency.<br/>\n",
    "Feather is plenty fast for the amount of data we are dealing with and is also a columnar format which is generally good for performance. Our dataframes already only contain the necessary columns and have been cleaned up so that the performance gain from using feather or parquet is minimal - it is more about the ease of use and consistency.\n",
    "\n",
    "## Data Analyzation / Visualization <a class=\"anchor\" id=\"data-visualization\"></a>\n",
    "After manually analyzing the data we have \"decided\" (following the projects requirements) to visualize the data in the following ways:\n",
    "- a few line and bar plots showing the development of the rent prices over the years as well as detailing the differences between the different cantons in the last year (2022)\n",
    "- a heatmap showing the development of the health insurance prices over the years as well as detailing the differences between the different cantons. The heatmap is interactive and allows for a more detailed view of the data over the 10-year timespan.\n",
    "\n",
    "The data is visualized in the notebook 'SC02_Gruppe_10_03_visualization.ipynb'. The visualizations are saved in the 'data/processed' folder of the repository. We display some of the visualizations in this report, do not generate them here again, though.\n",
    "\n",
    "### Details about 2022 (the latest year we have data on)\n",
    "\n",
    "![\"data/processed/stacked_bar_chart.jpg\"](./data/processed/stacked_bar_chart.jpg \"Cost per Canton in 2022\")\n",
    "\n",
    "The graph shows that the ratio between rent and health care cost is roughly the same in every canton and that has also prooven to be true for the previous years. The cantons with the highest rent prices are also the ones with the highest health care costs. The cantons with the lowest rent prices are also the ones with the lowest health care costs.\n",
    "\n",
    "### Development over the years\n",
    "![data/processed/total_category_race.gif](data/processed/total_category_race.gif)\n",
    "\n",
    "As this chart shows, the cost of living in Switzerland has been relatively stable over the years with the top 5 and botton 2 cantons being more or less the same over almost a decade. The middle cantons have been changing over the years, but the overall cost of living has been relatively stable.\n",
    "\n",
    "Simply based on this information the cantons Jura and Neuchâtel are the most attractive cantons to live in Switzerland, while the cantons of Zug and Zurich are the least attractive.\n",
    "\n",
    "It is <span style=\"font-weight: bold\">important to note</span> that this analysis is based on the basic cost of living in Switzerland and does not take into account other factors such as quality of life, job opportunities including average income, or other factors that may influence the decision to live in a particular canton.\n",
    "\n",
    "## Heatmap of Switzerland with the development of basic cost of living over the years (2013-2022)\n",
    "An alternative way to visualize the development of the cost of living in Switzerland is a heatmap. The heatmap shows the development of the cost of living in Switzerland over the years (2013-2022) for each canton. The heatmap is interactive and allows for a more detailed view of the data over the 10-year timespan. The disadvantage of the heatmap is that it is not as easy to compare the cantons as in the bar chart and that we have not found an interactive way to display the positions/categories of the cost of living in the heatmap.\n",
    "\n",
    "Saving the heatmap in a working html file has not proven successful, so we have to generate the heatmap allover again to view it."
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    "# Load GeoJSON-file\n",
    "geojson_path = './data/swissBOUNDARIES3D_1_3_TLM_KANTONSGEBIET.geojson'\n",
    "geo_data = gpd.read_file(geojson_path)\n",
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    "# Convert index to column\n",
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    "# Mapping of abbreviations (different state names für the geo_data than for cost data!)\n",
    "name_to_abbreviation = {\n",
    "    'Zürich': 'ZH', 'Bern': 'BE', 'Luzern': 'LU', 'Uri': 'UR', 'Schwyz': 'SZ', 'Obwalden': 'OW',\n",
    "    'Nidwalden': 'NW', 'Glarus': 'GL', 'Zug': 'ZG', 'Fribourg': 'FR', 'Solothurn': 'SO',\n",
    "    'Basel-Stadt': 'BS', 'Basel-Landschaft': 'BL', 'Schaffhausen': 'SH',\n",
    "    'Appenzell Ausserrhoden': 'AR', 'Appenzell Innerrhoden': 'AI', 'St. Gallen': 'SG',\n",
    "    'Graubünden': 'GR', 'Aargau': 'AG', 'Thurgau': 'TG', 'Ticino': 'TI', 'Vaud': 'VD',\n",
    "    'Valais': 'VS', 'Neuchâtel': 'NE', 'Genève': 'GE', 'Jura': 'JU'\n",
    "}\n",
    "\n",
    "# Replace full names with abbreviations geo dataframe\n",
    "geo_data['NAME'] = geo_data['NAME'].replace(name_to_abbreviation)\n",
    "\n",
    "# Save as new .json file\n",
    "geo_data.to_file('./data/swiss_kantone.geojson', driver='GeoJSON')\n",
    "\n",
    "# Use file as new path to create chloropleth map \n",
    "geojson_path = './data/swiss_kantone.geojson'\n",
    "\n",
    "# Denormalize the dataframe thus we get one row with all cost values for each year\n",
    "df_melted = df_total.melt(id_vars=['Kanton'], var_name='Year', value_name='Cost')\n",
    "\n",
    "# create interactive animation with plotly\n",
    "fig = px.choropleth(\n",
    "    df_melted,\n",
    "    geojson=geojson_path,\n",
    "    locations='Kanton',\n",
    "    color='Cost',\n",
    "    featureidkey=\"properties.NAME\",  # connect GeoJSON with dataframe\n",
    "    animation_frame='Year',  # Animation through available years\n",
    "    color_continuous_scale='Viridis',\n",
    "    title=\"Lebenshaltungskosten pro Kanton (2013–2022)\",\n",
    "    labels={'Cost': 'Lebenshaltungskosten (CHF)'}\n",
    ")\n",
    "\n",
    "# Adjust layout\n",
    "fig.update_geos(\n",
    "    visible=False,\n",
    "    fitbounds=\"locations\",\n",
    "    projection_type=\"mercator\"\n",
    ")\n",
    "fig.update_layout(\n",
    "    height=800,\n",
    "    width=1200,\n",
    "    coloraxis_colorbar=dict(\n",
    "        title=\"Lebenshaltungskosten (CHF)\",\n",
    "        tickprefix=\"CHF \",\n",
    "    )\n",
    ")\n",
    "\n",
    "# Show animation\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion <a class=\"anchor\" id=\"conclusion\"></a>\n",
    "The cost of living in Switzerland has been relatively stable over the years with the top 5 and botton 2 cantons being more or less the same over almost a decade. The middle cantons have been changing over the years, but the overall cost of living has been relatively stable there as well.\n",
    "\n",
    "The data used for this analysis does not take into account other factors such as quality of life, job opportunities including average income, or other factors that may influence the decision to live in a particular canton. The analysis is based on the basic cost of living in Switzerland.\n",
    "\n",
    "The results of this analysis do clearly not cover enough aspects to make a decision on where to live in Switzerland as the population has shown other preferred cantons to live in. The functions and visualizations are easily extendable to include more data and factors to make a more informed decision on where to live in Switzerland.\n",
    "\n",
    "Though the analysis is based on the basic cost of living in Switzerland, it is a good starting point for further analysis and decision making.\n",
    "\n",
    "So before you pack your bags and move to Jura or Neuchâtel, make sure to consider all the factors that are important to you when deciding where to live in Switzerland.<br/>\n",
    "It might be cheaper investing a few more hours into additional research than moving to a canton that is not the right fit for you.\n",
    "\n",
    "With that being said, we hope that this analysis has been helpful to you and wish you all the best in your decision making process.\n",
    "\n",
    "If you have any questions or feedback, please feel free to reach out to us.\n",
    "\n",
    "Thank you for reading!\n",
    "\n",
    "SC02_Group10\n",
    "\n",
    "<img src=\"./data/original/AnimatedChristmasTree.svg\" alt=\"Christmas Time\" style=\"width:200px; align: center;\"/>"
   ]
  }
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