Housing_for_Homelessness.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Data Processing"
      ],
      "metadata": {
        "id": "m4nLJhblZwWy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing\n",
        "import pandas as pd\n",
        "import openpyxl\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "import sys\n",
        "import matplotlib\n",
        "matplotlib.use('Agg')"
      ],
      "metadata": {
        "id": "YvP964hrv9ES"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# use this method to upload the PIT (not manually cleaned) and HIC (\"House Dataset.xlsx\" already briefly cleaned) datasets as files\n",
        "# => then later create dataframe variable for them\n",
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "id": "Tt2CAiRIZtbU",
        "outputId": "8d66ba94-c7c1-46d5-a9e3-e97cb22ed226"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b28762a6-2b50-4b83-ad82-5c32423b7502\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b28762a6-2b50-4b83-ad82-5c32423b7502\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### cleaning HIC dataset (housing dataset)"
      ],
      "metadata": {
        "id": "y947Y6etq9QL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_dict = pd.read_excel(\"psh.xlsx\", sheet_name=None)\n",
        "# df_dict = df_dict.pop(\"Revisions\", None)\n",
        "\n",
        "all_sheets = []\n",
        "for name, sheet in df_dict.items():\n",
        "  sheet = sheet.iloc[:, 0:2]\n",
        "  sheet['year'] = name\n",
        "  all_sheets.append(sheet)\n",
        "\n",
        "# psh means permanenet support housing\n",
        "psh = pd.concat(all_sheets)\n",
        "psh = psh.rename(columns={\"Total PSH Beds\": \"psh_beds\"})\n",
        "\n",
        "# plotting\n",
        "psh_coc = psh.groupby(by=[\"CoC\", \"year\"], dropna=False, as_index=False).mean()\n",
        "psh_coc_final = psh_coc.pivot(index=\"year\", columns=\"CoC\", values=\"psh_beds\")\n",
        "\n",
        "psh_coc_final.plot(figsize=(12, 10))\n",
        "plt.ylabel(\"psh_beds\")\n",
        "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),\n",
        "          fancybox=True, shadow=True, ncol=5)"
      ],
      "metadata": {
        "id": "lMujIn8Avs-0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ecf6da8-dd8c-4963-bdf2-d98245a53bc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/openpyxl/worksheet/header_footer.py:48: UserWarning: Cannot parse header or footer so it will be ignored\n",
            "  warn(\"\"\"Cannot parse header or footer so it will be ignored\"\"\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3afc9afa10>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### cleaning PIT dataset (homelessness data)"
      ],
      "metadata": {
        "id": "xSXSUxmrrA5u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2_dict = pd.read_excel(\"2007-2021-PIT-Counts-by-CoC.xlsx\", sheet_name=None)"
      ],
      "metadata": {
        "id": "GSOG0x6C3rz4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_sheets2 = []\n",
        "for name, sheet in df2_dict.items():\n",
        "  sheet1 = sheet.iloc[:, 0:185]\n",
        "  sheet2 = sheet1.iloc[:, [0]+[3]+[-1]]\n",
        "  sheet3 = sheet2.rename(columns={sheet2.columns[1]:\"all_homeless\", sheet2.columns[2]:\"chronic_homeless\"})\n",
        "  sheet4 = sheet3.loc[sheet3['CoC Number'].str.startswith('MA', na=False)]\n",
        "  sheet4[\"year\"] = name\n",
        "  all_sheets2.append(sheet4)\n",
        "\n",
        "pit_final = pd.concat(all_sheets2)\n",
        "pit_final = pit_final.rename(columns={\"CoC Number\": \"CoC\"})"
      ],
      "metadata": {
        "id": "LzziZFpvNhOV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4513e9fe-92d0-42fa-9dc9-0c7f5ad6b6c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# national PIT estimates of homelessness (OVERALL HOMELESSNESS)\n",
        "all_homeless = pit_final.groupby(by=[\"CoC\", \"year\"], dropna=False, as_index=False).mean()\n",
        "all_homeless = all_homeless.pivot(index=\"year\", columns=\"CoC\", values=\"all_homeless\") \n",
        "\n",
        "# plot\n",
        "all_homeless.plot(figsize=(12, 10))\n",
        "plt.ylabel(\"Total Homelessness\")\n",
        "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),\n",
        "          fancybox=True, shadow=True, ncol=5)"
      ],
      "metadata": {
        "id": "3X8vMiTL9kYx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "outputId": "cd1dbec7-9089-4654-c9de-7c8c464aaac9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7efe0dfb9990>"
            ]
          },
          "metadata": {},
          "execution_count": 150
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# chronicPIT estimates of homelessness (CHRONIC HOMELESSNESS)\n",
        "chronic_homeless = pit_final.groupby(by=[\"CoC\", \"year\"], dropna=False, as_index=False).mean()\n",
        "chronic_homeless = chronic_homeless.pivot(index=\"year\", columns=\"CoC\", values=\"chronic_homeless\") \n",
        "\n",
        "# plot\n",
        "chronic_homeless.plot(figsize=(12, 10))\n",
        "plt.ylabel(\"Chronic Homelessness\")\n",
        "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),\n",
        "          fancybox=True, shadow=True, ncol=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "OWH_r7qE9ocX",
        "outputId": "26033ab0-cac6-4780-ecc2-22192fa8d66b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7efe0de7f210>"
            ]
          },
          "metadata": {},
          "execution_count": 151
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Final data to use"
      ],
      "metadata": {
        "id": "mwUOL_0B9__s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# housing units (beds in permanent housing units)\n",
        "psh\n",
        "# homelessness overall\n",
        "all_homeless\n",
        "# homelessness chronic\n",
        "chronic_homeless\n",
        "# both overall and chronic\n",
        "pit_final\n",
        "\n",
        "# merging:\n",
        "final_data = pd.merge(psh, pit_final, how=\"inner\", on=[\"CoC\", \"year\"])\n",
        "final_data"
      ],
      "metadata": {
        "id": "f9gixmlQ-C3V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "e5d5c8c9-31d3-45cb-fc60-cc6b16d9481e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        CoC  psh_beds  year  all_homeless  chronic_homeless\n",
              "0    MA-500      3119  2021        4014.0              40.0\n",
              "1    MA-502       247  2021         927.0               0.0\n",
              "2    MA-503       259  2021         228.0               0.0\n",
              "3    MA-504       926  2021        2086.0               4.0\n",
              "4    MA-505       297  2021         267.0               0.0\n",
              "..      ...       ...   ...           ...               ...\n",
              "182  MA-511       275  2007         566.0             106.0\n",
              "183  MA-514       269  2007         502.0              19.0\n",
              "184  MA-515       108  2007          64.0               1.0\n",
              "185  MA-516       778  2007         956.0              62.0\n",
              "186  MA-519        78  2007          62.0              40.0\n",
              "\n",
              "[187 rows x 5 columns]"
            ],
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1d62f9ed-4c20-479d-a7bf-14bd0a42dec5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1d62f9ed-4c20-479d-a7bf-14bd0a42dec5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Single variable regression"
      ],
      "metadata": {
        "id": "onIGAayGSI74"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Regression analysis:"
      ],
      "metadata": {
        "id": "zyfpmM5dR7Cj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as st\n",
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "TrFN1M350Tsw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = 'all_homeless'\n",
        "X = ['psh_beds']\n",
        "\n",
        "pooled_y = final_data[y]\n",
        "pooled_X = final_data[X]\n",
        "\n",
        "pooled_X = sm.add_constant(pooled_X)\n",
        "pooled_olsr_model = sm.OLS(endog=pooled_y, exog=pooled_X)\n",
        "\n",
        "pooled_olsr_model_results = pooled_olsr_model.fit()\n",
        "\n",
        "print(pooled_olsr_model_results.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LZVXBetzv6t",
        "outputId": "3ee4f6fd-ca0f-4a95-ff97-7933ac39b1ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:           all_homeless   R-squared:                       0.547\n",
            "Model:                            OLS   Adj. R-squared:                  0.545\n",
            "Method:                 Least Squares   F-statistic:                     223.8\n",
            "Date:                Sun, 16 Oct 2022   Prob (F-statistic):           1.12e-33\n",
            "Time:                        13:07:10   Log-Likelihood:                -1459.7\n",
            "No. Observations:                 187   AIC:                             2923.\n",
            "Df Residuals:                     185   BIC:                             2930.\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const        305.2859     51.689      5.906      0.000     203.310     407.262\n",
            "psh_beds       0.5298      0.035     14.959      0.000       0.460       0.600\n",
            "==============================================================================\n",
            "Omnibus:                       63.337   Durbin-Watson:                   2.128\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              139.755\n",
            "Skew:                           1.560   Prob(JB):                     4.49e-31\n",
            "Kurtosis:                       5.863   Cond. No.                     1.73e+03\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.73e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On average, an additional bed in permanent supportive housing is associated with an increase in overall homelessness by 0.5298 individuals, holding other factors constant. Note that this model did not consider the CoC and year effect, that is why the estimated coefficient does not make sense. <br> We then proposed another model that can fit better: two-way fixed-effect model"
      ],
      "metadata": {
        "id": "eq1967tg-P3m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fixed-effect regression"
      ],
      "metadata": {
        "id": "UAxjZGp7SWaT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install linearmodels\n",
        "from linearmodels.panel import PanelOLS"
      ],
      "metadata": {
        "id": "D5EKolt1n6Zb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_fe = final_data[[\"CoC\", \"year\", \"psh_beds\", \"all_homeless\"]]\n",
        "\n",
        "#convert year to datetime\n",
        "data_fe['year'] = pd.to_datetime(data_fe['year'], format='%Y')\n",
        "# set panel data\n",
        "data_fe = data_fe.set_index(['CoC', 'year'])  \n",
        "\n",
        "exog_vars = [\"psh_beds\"]\n",
        "# exog = sm.add_constant(data_fe[exog_vars])\n",
        "mod = PanelOLS(data_fe[\"all_homeless\"], data_fe[exog_vars], entity_effects=True, time_effects=True)\n",
        "fe_te_res = mod.fit()\n",
        "print(fe_te_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYWOlPfdSZez",
        "outputId": "99b587fb-991c-47d2-9185-0c64d1249fbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                          PanelOLS Estimation Summary                           \n",
            "================================================================================\n",
            "Dep. Variable:           all_homeless   R-squared:                        0.0640\n",
            "Estimator:                   PanelOLS   R-squared (Between):             -0.6908\n",
            "No. Observations:                 187   R-squared (Within):               0.0363\n",
            "Date:                Sun, Oct 16 2022   R-squared (Overall):             -0.6533\n",
            "Time:                        12:28:31   Log-likelihood                   -1320.0\n",
            "Cov. Estimator:            Unadjusted                                           \n",
            "                                        F-statistic:                      10.798\n",
            "Entities:                          14   P-value                           0.0013\n",
            "Avg Obs:                       13.357   Distribution:                   F(1,158)\n",
            "Min Obs:                       2.0000                                           \n",
            "Max Obs:                       15.000   F-statistic (robust):             10.798\n",
            "                                        P-value                           0.0013\n",
            "Time periods:                      15   Distribution:                   F(1,158)\n",
            "Avg Obs:                       12.467                                           \n",
            "Min Obs:                       12.000                                           \n",
            "Max Obs:                       14.000                                           \n",
            "                                                                                \n",
            "                             Parameter Estimates                              \n",
            "==============================================================================\n",
            "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
            "------------------------------------------------------------------------------\n",
            "psh_beds      -0.2580     0.0785    -3.2860     0.0013     -0.4130     -0.1029\n",
            "==============================================================================\n",
            "\n",
            "F-test for Poolability: 20.215\n",
            "P-value: 0.0000\n",
            "Distribution: F(27,158)\n",
            "\n",
            "Included effects: Entity, Time\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On average, an additional bed in permanent supportive housing is associated with a reduction in overall homelessness by 0.2580 individuals, holding other factors constant and under the consideration of CoC and year effects"
      ],
      "metadata": {
        "id": "ifmstspkmRve"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot\n",
        "plt.scatter(data_fe[\"psh_beds\"], data_fe[\"all_homeless\"], alpha=0.5)\n",
        "plt.xlabel(\"PSH beds\")\n",
        "plt.ylabel(\"Overall homelessness\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "kCBcmu5yoSQ8",
        "outputId": "a226e9a3-dcab-48fa-a735-29ae3a7d285a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}