Repository: osm-flex/osm-flex (language: python)

Issue:
Feature/add filter polygons
Add mini-feature to filter out polygons.


Discussion / hints:

Fix patch:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 6173631..c63cf5d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,4 +1,23 @@
 # Changelog
+## v1.1.0
+
+Release date: 2023-11-21
+
+### Description
+
+Second release. 
+
+### Added
+* simplify module for simple cleaning functions of OSM parsing output
+
+### Changed
+
+* clipping with osmconvert can now handle parentfiles > 2 GB
+
+### Fixed
+
+* Fix  [Issue #25](https://github.com/osm-flex/osm-flex/issues/25).
+* Fix  [Issue #15](https://github.com/osm-flex/osm-flex/issues/15).
 
 ## v1.0.1
 
diff --git a/doc/0_basic_usage.ipynb b/doc/0_basic_usage.ipynb
index b0e0b84..2e58909 100644
--- a/doc/0_basic_usage.ipynb
+++ b/doc/0_basic_usage.ipynb
@@ -93,7 +93,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using pre-written wrappers for certain critical infrastructure types (check which ones are available), with the method `extract_cis`. They can be further configured in the `DICT_CIS_OSM` dictionary located in the config file "
+    "**Option 1:** \n",
+    "\n",
+    "Using pre-written wrappers for certain critical infrastructure types (check which ones are available), with the method `extract_cis`. \n",
+    "\n",
+    "They can be further configured in the `DICT_CIS_OSM` dictionary located in the config file "
    ]
   },
   {
@@ -127,6 +131,15 @@
     "? ex.extract_cis"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_che_dump = osm_flex.config.OSM_DATA_DIR.joinpath('switzerland-latest.osm.pbf')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -143,7 +156,6 @@
     }
    ],
    "source": [
-    "path_che_dump = osm_flex.config.OSM_DATA_DIR.joinpath('switzerland-latest.osm.pbf')\n",
     "gdf_che_mainroad = ex.extract_cis(path_che_dump, 'main_road')"
    ]
   },
@@ -174,12 +186,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using key and value tags from OSM (check [https://taginfo.openstreetmap.org/]) with the method `extract`"
+    "**Option 2:** \n",
+    "\n",
+    "Using key (and value) tags from OSM (check [https://taginfo.openstreetmap.org/]) with the method `extract`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_ca_dump = osm_flex.config.OSM_DATA_DIR.joinpath('central-america-latest.osm.pbf')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -187,13 +210,13 @@
      "output_type": "stream",
      "text": [
       "Warning 1: Non closed ring detected. To avoid accepting it, set the OGR_GEOMETRY_ACCEPT_UNCLOSED_RING configuration option to NO\n",
-      "extract multipolygons:   6%|█████████▋                                                                                                                                                           | 446/7578 [00:51<09:33, 12.43it/s]Warning 1: Non closed ring detected. To avoid accepting it, set the OGR_GEOMETRY_ACCEPT_UNCLOSED_RING configuration option to NO\n",
-      "extract multipolygons: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7578/7578 [01:34<00:00, 80.03it/s]\n"
+      "extract multipolygons:   6%|█████▊                                                                                             | 446/7578 [00:58<10:53, 10.91it/s]Warning 1: Non closed ring detected. To avoid accepting it, set the OGR_GEOMETRY_ACCEPT_UNCLOSED_RING configuration option to NO\n",
+      "extract multipolygons: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 7578/7578 [01:39<00:00, 76.31it/s]\n"
      ]
     }
    ],
    "source": [
-    "path_ca_dump = osm_flex.config.OSM_DATA_DIR.joinpath('central-america-latest.osm.pbf')\n",
+    "# Example using keys and value constraints \n",
     "gdf_ca_forest = ex.extract(path_ca_dump,'multipolygons',\n",
     "                            ['landuse', 'name'], \n",
     "                            \"landuse='forest'\")"
@@ -222,6 +245,48 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract multipolygons: 100%|█████████████████████████████████████████████████████████████████████████████████████████| 2608882/2608882 [02:02<00:00, 21261.05it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example using only a key (i.e., parse all items which have a non-null entry for the first key)\n",
+    "gdf_che_bldgs = ex.extract(path_che_dump,'multipolygons', \n",
+    "                          ['building', 'name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "gdf_che_bldgs.plot(ax=ax)\n",
+    "fig.suptitle('All buildings in Switzerland')\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -233,7 +298,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For custom data dumps, the planet, regional or country files can be clipped (=cut) to a user-defined geographical extent, and saved as new osm.pbf file. Instructions can be found in tutorial 1_clipping_shapes."
+    "For custom data dumps, the planet, regional or country files can be clipped (=cut) to a user-defined geographical extent and saved as new osm.pbf file using the `clip` module. Instructions can be found in tutorial 1_clipping_shapes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optional Step: Simplifying data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parsing data can result in duplicates or near-dupicates, or yield too many results. A few simple methods are provided in the `simplify` module to handle this. Instructions can be found in tutorial 2_simplifications."
    ]
   },
   {
diff --git a/doc/1_clipping_shapes.ipynb b/doc/1_clipping_shapes.ipynb
index b94967c..458953c 100644
--- a/doc/1_clipping_shapes.ipynb
+++ b/doc/1_clipping_shapes.ipynb
@@ -46,18 +46,18 @@
    "source": [
     "**Important Note 3:**\n",
     "\n",
-    "The following examples are demonstrated using osmosis. For osmconvert, simply change the keyword argument `kernel` to `'osmconvert'`."
+    "`'osmconvert'` features optional parameters `--complete-ways`, `--complete-multipolygons` and `--complete-boundaries`. These are useful if objects which cross clipping borders should not be cut off. If you want this, the function `osm_flex.clip._build_osmconvert_cmd()` manually needs to be amended (comment out those options). Howerver, this will only run for parent files < 2 GB. Hence, clipping from e.g., the planet or the europe.osm.pbf files is not possible with these options. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Loading necessary data packages\n",
     "import sys\n",
-    "sys.path.append('/Users/evelynm/osm-flex/src') # your path to osm-flex/src\n",
+    "sys.path.append('') # your path to osm-flex/src\n",
     "\n",
     "import osm_flex.download as dl\n",
     "import osm_flex.extract as ex\n",
@@ -65,9 +65,23 @@
     "import osm_flex.clip as cp"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 1: Clipping a province from a country file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following example uses the `osmosis` kernel. If you installed `osmconvert` instead, just change the keyword arguement accordingly, and check that the executable is either placed in the path specified in `osm_flex.config.OSMCONVERT_PATH`, or that this variable is adjusted accordingly."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -90,7 +104,7 @@
        "<POLYGON ((-87.319 14.667, -87.315 14.668, -87.314 14.67, -87.313 14.676, -8...>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -102,44 +116,113 @@
     "francisco_morazan_shp"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cp.clip_from_shapes([francisco_morazan_shp], \n",
+    "                    osmpbf_output=osm_flex.config.OSM_DATA_DIR.joinpath('francisco_morazan.osm.pbf'),\n",
+    "                    osmpbf_clip_from=osm_flex.config.OSM_DATA_DIR.joinpath('honduras-latest.osm.pbf'),\n",
+    "                    kernel='osmosis', overwrite=True)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following examples use the `osmosis` kernel. If you installed `osmconvert` instead, just change the keyword arguement accordingly, and check that the executable is either placed in the path specified in `osm_flex.config.OSMCONVERT_PATH`, or that this variable is adjusted accordingly."
+    "### Example 2: Clipping a country from a region file"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Downloading a larger \"parent\" file to clip from: Europe, from geofabrik.de \n",
+    "# --> saved to OSM_DATA_DIR/europe-latest.osm.pbf\n",
+    "dl.get_region_geofabrik('europe')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
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+      ],
+      "text/plain": [
+       "<POLYGON ((10.454 46.864, 10.449 46.832, 10.445 46.823, 10.439 46.817, 10.41...>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain a country polygon (Switzerland) and clip the europe file to it:\n",
+    "admin_che  = cp.get_country_shape('CHE')\n",
+    "admin_che"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using `osmconvert` (this takes a while, ca. 5 - 10 mins)\n",
+    "cp.clip_from_shapes([admin_che], \n",
+    "                    osmpbf_output=osm_flex.config.OSM_DATA_DIR.joinpath('che_clipped.osm.pbf'),\n",
+    "                    osmpbf_clip_from=osm_flex.config.OSM_DATA_DIR.joinpath('europe-latest.osm.pbf'),\n",
+    "                    kernel='osmconvert', overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Apr 21, 2023 2:58:25 PM org.openstreetmap.osmosis.core.Osmosis run\n",
+      "Nov 02, 2023 2:44:20 PM org.openstreetmap.osmosis.core.Osmosis run\n",
       "INFO: Osmosis Version 0.48.3\n",
-      "Apr 21, 2023 2:58:26 PM org.openstreetmap.osmosis.core.Osmosis run\n",
+      "Nov 02, 2023 2:44:20 PM org.openstreetmap.osmosis.core.Osmosis run\n",
       "INFO: Preparing pipeline.\n",
-      "Apr 21, 2023 2:58:26 PM org.openstreetmap.osmosis.core.Osmosis run\n",
+      "Nov 02, 2023 2:44:20 PM org.openstreetmap.osmosis.core.Osmosis run\n",
       "INFO: Launching pipeline execution.\n",
-      "Apr 21, 2023 2:58:26 PM org.openstreetmap.osmosis.core.Osmosis run\n",
+      "Nov 02, 2023 2:44:20 PM org.openstreetmap.osmosis.core.Osmosis run\n",
       "INFO: Pipeline executing, waiting for completion.\n",
-      "Apr 21, 2023 2:58:29 PM org.openstreetmap.osmosis.core.Osmosis run\n",
+      "Nov 02, 2023 3:10:10 PM org.openstreetmap.osmosis.core.Osmosis run\n",
       "INFO: Pipeline complete.\n",
-      "Apr 21, 2023 2:58:29 PM org.openstreetmap.osmosis.core.Osmosis run\n",
-      "INFO: Total execution time: 4062 milliseconds.\n"
+      "Nov 02, 2023 3:10:10 PM org.openstreetmap.osmosis.core.Osmosis run\n",
+      "INFO: Total execution time: 1550579 milliseconds.\n"
      ]
     }
    ],
    "source": [
-    "cp.clip_from_shapes([francisco_morazan_shp], \n",
-    "                    osmpbf_output=osm_flex.config.OSM_DATA_DIR.joinpath('francisco_morazan.osm.pbf'),\n",
-    "                    osmpbf_clip_from=osm_flex.config.OSM_DATA_DIR.joinpath('honduras-latest.osm.pbf'),\n",
+    "# Using `osmosis` (this takes a while, ca. 10-20 mins)\n",
+    "cp.clip_from_shapes([admin_che], \n",
+    "                    osmpbf_output=osm_flex.config.OSM_DATA_DIR.joinpath('che_clipped_osmosis.osm.pbf'),\n",
+    "                    osmpbf_clip_from=osm_flex.config.OSM_DATA_DIR.joinpath('europe-latest.osm.pbf'),\n",
     "                    kernel='osmosis', overwrite=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 3: Clipping a bounding box from a file"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -173,6 +256,13 @@
     "                  kernel='osmosis',overwrite=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 4: Clipping a file using another .poly file"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 12,
diff --git a/doc/2_simplifications.ipynb b/doc/2_simplifications.ipynb
new file mode 100644
index 0000000..388234d
--- /dev/null
+++ b/doc/2_simplifications.ipynb
@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ce63d190",
+   "metadata": {},
+   "source": [
+    "## Simplifying parsed data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a462b28",
+   "metadata": {},
+   "source": [
+    "The following functions provide some simple methods to refine data parsed from OSM. \n",
+    "For more elaborate geospatial functions, designed to simplify the geometry of shapes and make them more coherent, please refer to bespoke packages. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "13b9421f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading necessary data packages\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('/Users/evelynm/osm-flex/src') \n",
+    "\n",
+    "import osm_flex.download as dl\n",
+    "import osm_flex.extract as ex\n",
+    "import osm_flex.config\n",
+    "import osm_flex.simplify as sy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24370cf6",
+   "metadata": {},
+   "source": [
+    "### Example: Remove (near-)duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8a5ac07a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get an OSM dataset for illustration purposes\n",
+    "iso3 = 'CHE'\n",
+    "dl.get_country_geofabrik(iso3)\n",
+    "path_che_dump = osm_flex.config.OSM_DATA_DIR.joinpath('switzerland-latest.osm.pbf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1256b39d",
+   "metadata": {},
+   "source": [
+    "Sometimes, places can be tagged as points (POIs) or as their explicit shape (e.g. building polygons, or rooms / areas within larger buildings). This results effectively in near-duplicates.\n",
+    "The following examples illustrate this with the pre-written healthcare parser:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9563aa03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract points: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1943/1943 [00:15<00:00, 128.68it/s]\n",
+      "extract multipolygons: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 1407/1407 [00:54<00:00, 25.73it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Query yields point and multi-polygon data \n",
+    "gdf_che_health = ex.extract_cis(path_che_dump, 'healthcare')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a36c9846",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of results: 3350\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Number of results: {len(gdf_che_health)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f7c92f79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of results after removing points contained in polygons: 3257\n"
+     ]
+    }
+   ],
+   "source": [
+    "gdf_che_health = sy.remove_contained_points(gdf_che_health)\n",
+    "print(f'Number of results after removing points contained in polygons: {len(gdf_che_health)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8c276652",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of results after removing polygons contained in larger polygons: 2455\n"
+     ]
+    }
+   ],
+   "source": [
+    "gdf_che_health = sy.remove_contained_polys(gdf_che_health)\n",
+    "print(f'Number of results after removing polygons contained in larger polygons: {len(gdf_che_health)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1cd7939e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of results after removing exact geometrical duplicates: 2455\n"
+     ]
+    }
+   ],
+   "source": [
+    "gdf_che_health = sy.remove_exact_duplicates(gdf_che_health)\n",
+    "print(f'Number of results after removing exact geometrical duplicates: {len(gdf_che_health)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ff9c87a",
+   "metadata": {},
+   "source": [
+    "### Example: Remove small polygons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c9d5bfdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract multipolygons: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 76220/76220 [00:41<00:00, 1822.98it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "gdf_che_forest = ex.extract(path_che_dump,'multipolygons', ['landuse', 'name'], \"landuse='forest'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dedf39ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of results: 76220\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Number of results: {len(gdf_che_forest)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b26d562b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of results after removal of small polygons: 74484\n"
+     ]
+    }
+   ],
+   "source": [
+    "gdf_che_forest = gdf_che_forest.to_crs('epsg:21781') # metre-based CRS for Switzerland\n",
+    "min_area = 100\n",
+    "gdf_che_forest = sy.remove_small_polygons(gdf_che_forest, min_area) # remove all areas < 100m2 (always in units of respective CRS)\n",
+    "print(f'Number of results after removal of small polygons: {len(gdf_che_forest)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92fd81f3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
index bc6f4cc..41c3c1c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "osm-flex"
-version = "1.0.1"
+version = "1.1.0"
 maintainers = [
   { name = "Elco Koks", email = "elco.koks@vu.nl" },
 ]
diff --git a/src/osm_flex/clip.py b/src/osm_flex/clip.py
index d784e38..b025f2e 100644
--- a/src/osm_flex/clip.py
+++ b/src/osm_flex/clip.py
@@ -160,19 +160,23 @@ def _build_osmconvert_cmd(shape, osmpbf_clip_from, osmpbf_output):
         file path to planet.osm.pbf or other osm.pbf file to clip
     osmpbf_output : str or pathlib.Path
         file path (incl. name & ending) under which extract will be stored
+        
+    Note
+    -----
+    If cross-border multi-polygons or lines should be retained, comment out
+    below options '--complete-ways' and '--complete-multipolygons'. This only
+    works with parent files < 2GB.
     """
 
     if isinstance(shape, (pathlib.Path, str)):
         return [str(OSMCONVERT_PATH), str(osmpbf_clip_from), f'-B={str(shape)}',
-                '--complete-ways', '--complete-multipolygons',
                 f'-o={osmpbf_output}'
-                ]
+                ] #'--complete-ways', '--complete-multipolygons' # add option
     if isinstance(shape[0], (float, int)):
         return [str(OSMCONVERT_PATH), str(osmpbf_clip_from),
                 '-b='+str(shape[0])+','+str(shape[1])+','+str(shape[2])+','+str(shape[3]),
-                '--complete-ways', '--complete-multipolygons',
                 f'-o={osmpbf_output}'
-                ]
+                ] # '--complete-ways', '--complete-multipolygons' # add option
 
 def _osmconvert_clip(shape, osmpbf_clip_from, osmpbf_output,
                      overwrite=False):
@@ -334,10 +338,11 @@ def clip_from_poly(poly_file, osmpbf_clip_from, osmpbf_output,
 
     Note
     ----
-    This function uses by default the command line tool osmosis
-    to cut out new osm.pbf files from the original ones.
-    Installation instructions (windows, linux, macos) - see
-    https://wiki.openstreetmap.org/wiki/Osmosis/Installation
+    This function uses the command line tool osmosis or osmconvert to clip 
+    new osm.pbf files from the original ones.
+    Installation instructions (windows, linux, apple) - see
+    https://wiki.openstreetmap.org/wiki/Osmosis/Installation or 
+    https://wiki.openstreetmap.org/wiki/Osmconvert
     """
     if kernel == 'osmosis':
         _osmosis_clip(poly_file, osmpbf_clip_from, osmpbf_output, overwrite)
@@ -374,10 +379,12 @@ def clip_from_shapes(shape_list, osmpbf_clip_from, osmpbf_output,
 
     Note
     ----
-    This function uses the command line tool osmosis to cut out new
-    osm.pbf files from the original ones.
+    This function uses the command line tool osmosis or osmconvert to clip 
+    new osm.pbf files from the original ones.
     Installation instructions (windows, linux, apple) - see
-    https://wiki.openstreetmap.org/wiki/Osmosis/Installation
+    https://wiki.openstreetmap.org/wiki/Osmosis/Installation or 
+    https://wiki.openstreetmap.org/wiki/Osmconvert 
+    
     """
 
     shape_list = _simplify_shapelist(shape_list)
diff --git a/src/osm_flex/extract.py b/src/osm_flex/extract.py
index 0419ce0..67cc02a 100644
--- a/src/osm_flex/extract.py
+++ b/src/osm_flex/extract.py
@@ -27,7 +27,7 @@
 gdal.SetConfigOption("OSM_CONFIG_FILE", str(OSM_CONFIG_FILE))
 
 
-def _query_builder( geo_type, constraint_dict):
+def _query_builder(geo_type, constraint_dict):
     """
     This function builds an SQL query from the values passed to the extract()
     function.
@@ -48,14 +48,16 @@ def _query_builder( geo_type, constraint_dict):
     for key in constraint_dict['osm_keys']:
         query+= ","+ key
     # filter condition(s)
-    query+= " FROM " + geo_type + " WHERE " + constraint_dict['osm_query']
-
+    if constraint_dict['osm_query'] is not None:
+        query+= " FROM " + geo_type + " WHERE " + constraint_dict['osm_query']
+    else:
+        query += " FROM " + geo_type + f" WHERE {constraint_dict['osm_keys'][0]} IS NOT NULL"
     return query
 
-def extract(osm_path, geo_type, osm_keys, osm_query):
+def extract(osm_path, geo_type, osm_keys, osm_query=None):
     """
     Function to extract geometries and tag info for entires in the OSM file
-    matching certain OSM key-value constraints.
+    matching certain OSM keys, or key-value constraints.
     from an OpenStreetMap osm.pbf file.
 
     Parameters
@@ -68,8 +70,9 @@ def extract(osm_path, geo_type, osm_keys, osm_query):
         a list with all the osm keys that should be reported as columns in
         the output gdf.
     osm_query : str
-        query string of the syntax
-        "key(='value') (and/or further queries)".
+        optional. query string of the syntax
+        "key='value' (and/or further queries)". If left empty, all objects
+        for which the first entry of osm_keys is not Null will be parsed.
         See examples in DICT_CIS_OSM in case of doubt.
 
     Returns
@@ -91,6 +94,10 @@ def extract(osm_path, geo_type, osm_keys, osm_query):
     search dict, but not in the osmconf.ini
     E.g. tower:type is called tower_type, since it would interfere with the
     SQL syntax otherwise, but still tower:type in the osmconf.ini
+    3) If the osm_query is left empty (None), then all objects will be parsed
+    for which the first entry of osm_keys is not Null. E.g. if osm_keys =
+    ['building', 'name'] and osm_query = None, then all items matching
+    building=* will be parsed.
 
     See also
     --------
@@ -102,7 +109,7 @@ def extract(osm_path, geo_type, osm_keys, osm_query):
     """
     if not Path(osm_path).is_file():
         raise ValueError(f"the given path is not a file: {osm_path}")
-    
+
     osm_path = str(osm_path)
     constraint_dict = {
         'osm_keys' : osm_keys,
@@ -168,7 +175,7 @@ def extract_cis(osm_path, ci_type):
 
     # features consisting in multipolygon results:
     elif ci_type in ['air']:
-        gdf = extract(osm_path, 'multipolygons', 
+        gdf = extract(osm_path, 'multipolygons',
                       DICT_CIS_OSM[ci_type]['osm_keys'],
                       DICT_CIS_OSM[ci_type]['osm_query'])
 
diff --git a/src/osm_flex/simplify.py b/src/osm_flex/simplify.py
new file mode 100644
index 0000000..dba410a
--- /dev/null
+++ b/src/osm_flex/simplify.py
@@ -0,0 +1,119 @@
+"""
+This file is part of OSM-flex.
+Copyright (C) 2023 OSM-flex contributors listed in AUTHORS.
+OSM-flex is free software: you can redistribute it and/or modify it under the
+terms of the GNU General Public License as published by the Free
+Software Foundation, version 3.
+OSM-flex is distributed in the hope that it will be useful, but WITHOUT ANY
+WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+PARTICULAR PURPOSE.  See the GNU General Public License for more details.
+-----
+simplification functions
+"""
+
+import geopandas as gpd
+import numpy as np
+
+def remove_small_polygons(gdf, min_area):
+    """Remove (multi-)polygons of area smaller than min_area
+    Points and lines are untouched.
+
+    Note: a buffer of 1e-10 is added to invalid geometries
+
+    Parameters
+    ----------
+    gdf : GeoDataFrame
+        geodataframe with polygons
+    min_area : float
+        minimal value of area
+
+    Return
+    ------
+    GeoDataFrame:
+        entry geodataframe without (multi-)polygons smaller than min_area
+    """
+
+    gdf_temp = gdf.copy()
+
+    def make_valid(geometry):
+        if geometry.is_valid:
+            return geometry
+        return geometry.buffer(1e-10)
+
+    gdf_temp['geometry'] = gdf_temp.apply(lambda row: make_valid(row.geometry),
+                                          axis=1)
+
+    return gdf_temp[(gdf_temp['geometry'].area > min_area) |
+                    (gdf_temp['geometry'].area == 0)].reset_index(drop=True)
+
+
+def remove_contained_points(gdf_p_mp):
+    """
+    from a GeoDataFrame containing points and (multi-)polygons, remove those
+    points that are contained in a multipolygons entry.
+    Resets the index of the dataframe.
+
+    Parameters
+    ----------
+    gdf_p_mp : gpd.GeoDataFrame
+        GeoDataFrame containing entries with point and (multi-)polygon geometry
+    """
+
+    gdf_p_mp = gdf_p_mp.reset_index(drop=True)
+
+    ind_dupl = np.unique(gpd.sjoin(gdf_p_mp[gdf_p_mp.geometry.type=='Point'],
+              gdf_p_mp[(gdf_p_mp.geometry.type=='MultiPolygon')|
+                       (gdf_p_mp.geometry.type=='Polygon')],
+              predicate='within').index)
+
+    return gdf_p_mp.drop(index=ind_dupl).reset_index(drop=True)
+
+
+def remove_contained_polys(gdf):
+    """
+    from a GeoDataFrame containing (multi-)polygons (and potentially other
+    geometries), remove those polygon entries that are already fully
+    contained in another polygon entries. Removes smaller polygons within 
+    polygons and full duplicates, but leaves contained points untouched 
+    (see remove_contained_points() for this).
+
+    Resets the index of the dataframe.
+
+    Parameters
+    ----------
+    gdf : gpd.GeoDataFrame
+        GeoDataFrame containing entries with (multi-)polygon geometry
+    """
+
+    gdf = gdf.reset_index(drop=True)
+
+    contained = gpd.sjoin(
+        gdf[(gdf.geometry.type=='MultiPolygon')| (gdf.geometry.type=='Polygon')],
+        gdf[(gdf.geometry.type=='MultiPolygon')| (gdf.geometry.type=='Polygon')],
+        predicate='contains'
+        )
+
+    subset = contained[contained.index != contained.index_right]
+    to_drop = set(subset.index_right) - set(subset.index)
+
+    return gdf.drop(index=to_drop).reset_index(drop=True)
+
+
+def remove_exact_duplicates(gdf):
+    """
+    from a GeoDataFrame containing any sort of geometries, remove those entries
+    which already have an exact duplicate geometry entry.
+
+    Resets the index of the dataframe.
+
+    Parameters
+    ----------
+    gdf : gpd.GeoDataFrame
+        GeoDataFrame containing any types of geometry
+    """
+
+    gdf = gdf.reset_index(drop=True)
+
+    geom_wkb = gdf["geometry"].apply(lambda geom: geom.wkb)
+
+    return gdf.loc[geom_wkb.drop_duplicates().index].reset_index(drop=True)


Test patch:
diff --git a/tests/test_clip.py b/tests/test_clip.py
index 2c14b52..6a597be 100644
--- a/tests/test_clip.py
+++ b/tests/test_clip.py
@@ -129,8 +129,6 @@ def test__build_osmconvert_cmd(self):
         expected_result = [str(OSMCONVERT_PATH),
          '/path/to/planet.osm.pbf',
          '-b=0,0,1,1',
-         '--complete-ways',
-         '--complete-multipolygons',
          '-o=/path/to/extract.osm.pbf']
         self.assertEqual(result, expected_result)
         
@@ -142,8 +140,6 @@ def test__build_osmconvert_cmd(self):
         expected_result = [str(OSMCONVERT_PATH),
          '/path/to/planet.osm.pbf',
          '-B=/path/to/shape.poly',
-         '--complete-ways',
-         '--complete-multipolygons',
          '-o=/path/to/extract.osm.pbf']
         self.assertEqual(result, expected_result)
         
diff --git a/tests/test_extract.py b/tests/test_extract.py
index 8fa5d65..031f742 100644
--- a/tests/test_extract.py
+++ b/tests/test_extract.py
@@ -14,10 +14,11 @@
 import unittest
 import geopandas as gpd
 import numpy as np
+import shapely as sh
 from osm_flex.extract import extract, extract_cis, _query_builder
 from pathlib import Path
 
-PATH_TEST_DATA = Path(__file__).parent / 'data' 
+PATH_TEST_DATA = Path(__file__).parent / 'data'
 OSM_FILE = PATH_TEST_DATA / 'test.osm.pbf'
 
 class TestExtractionFunctions(unittest.TestCase):
@@ -26,24 +27,53 @@ def test_extract(self):
         """
         test function extract()
         """
-        gdf_mp = extract(OSM_FILE, 'multipolygons',  ['name', 'building'], 
+        gdf_mp = extract(OSM_FILE, 'multipolygons',  ['name', 'building'],
                          "building='yes'")
         self.assertIsInstance(gdf_mp, gpd.GeoDataFrame)
-        self.assertEqual(set(gdf_mp.columns), 
+        self.assertEqual(set(gdf_mp.columns),
                          set(['osm_id', 'building', 'name', 'geometry']))
-        self.assertEqual(np.unique(gdf_mp.geometry.type), 
+        self.assertEqual(np.unique(gdf_mp.geometry.type),
                          np.array(['MultiPolygon']))
         self.assertTrue(len(gdf_mp)==4202)
-        
-        gdf_line = extract(OSM_FILE, 'lines', ['name', 'highway'], 
+
+        gdf_mp2 = extract(OSM_FILE, 'multipolygons',  ['building', 'name'])
+        self.assertIsInstance(gdf_mp2, gpd.GeoDataFrame)
+        self.assertEqual(set(gdf_mp2.columns),
+                         set(['osm_id', 'building', 'name', 'geometry']))
+        self.assertEqual(np.unique(gdf_mp2.geometry.type),
+                         np.array(['MultiPolygon']))
+        self.assertEqual(len(gdf_mp2),5050)
+        self.assertFalse(any(elem is None for elem in gdf_mp2.building))
+
+
+        gdf_line = extract(OSM_FILE, 'lines', ['name', 'highway'],
                            "highway='residential'")
         self.assertIsInstance(gdf_line, gpd.GeoDataFrame)
-        self.assertEqual(set(gdf_line.columns), 
+        self.assertEqual(set(gdf_line.columns),
                          set(['osm_id', 'name', 'highway', 'geometry']))
-        self.assertEqual(np.unique(gdf_line.geometry.type), 
+        self.assertEqual(np.unique(gdf_line.geometry.type),
                          np.array(['LineString']))
         self.assertTrue(len(gdf_line)==1807)
-        
+
+        gdf_line2 = extract(OSM_FILE, 'lines', ['highway','name'], None)
+        self.assertIsInstance(gdf_line2, gpd.GeoDataFrame)
+        self.assertEqual(set(gdf_line2.columns),
+                         set(['osm_id', 'name', 'highway', 'geometry']))
+        self.assertEqual(np.unique(gdf_line2.geometry.type),
+                         np.array(['LineString']))
+        self.assertEqual(len(gdf_line2),3266)
+        self.assertFalse(any(elem is None for elem in gdf_line2.highway))
+
+        gdf_line3 = extract(OSM_FILE, 'lines', ['highway'], None)
+        self.assertIsInstance(gdf_line3, gpd.GeoDataFrame)
+        self.assertEqual(set(gdf_line3.columns),
+                         set(['osm_id', 'highway', 'geometry']))
+        self.assertEqual(np.unique(gdf_line3.geometry.type),
+                         np.array(['LineString']))
+        self.assertEqual(len(gdf_line3),3266)
+        self.assertFalse(any(elem is None for elem in gdf_line3.highway))
+
+
         # TODO: test with invalid geo_type
 
     def test_extract_cis(self):
@@ -55,15 +85,15 @@ def test_extract_cis(self):
         self.assertTrue(np.any([entry =='school' for entry in gdf_schools.amenity]))
         self.assertTrue(len(gdf_schools)==215)
         self.assertTrue('amenity' in gdf_schools.columns)
-        
+
         gdf_roads = extract_cis(OSM_FILE, 'road')
         self.assertTrue(np.any([entry =='tertiary' for entry in gdf_roads.highway]))
         self.assertTrue(np.any([entry =='residential' for entry in gdf_roads.highway]))
         self.assertTrue(len(gdf_roads)==2603)
         self.assertTrue('name' in gdf_roads.columns)
-        
+
         # TODO: test with invalid ci-argument
-        
+
     def test__query_builder(self):
         """
         test function _query_builder()
@@ -74,18 +104,27 @@ def test__query_builder(self):
         query = _query_builder(geo_type, constraint_dict)
         self.assertEqual(query,
                          "SELECT osm_id,name,highway FROM points WHERE highway='residential'")
-      
+
+        geo_type = 'points'
+        constraint_dict = {'osm_keys': ['name', 'highway'],
+                           'osm_query' : None}
+        query = _query_builder(geo_type, constraint_dict)
+        self.assertEqual(query,
+                         'SELECT osm_id,name,highway FROM points WHERE name IS NOT NULL')
+
+
     def test_qb_error(self):
         geo_type = 'points'
         constraint_dict2 = {'wrong_key': ['name', 'highway'],
                            'osm_query' :  "highway='residential'"}
-        
+
         with self.assertRaises(KeyError) as context:
             _query_builder(geo_type, constraint_dict2)
         # TODO: how to check now that raises either keyerror or key error with message
         #self.assertTrue('osm_keys' in context.exception)
         #self.assertTrue(KeyError in context.exception)
-       
+
+        
 if __name__ == "__main__":
     TESTS = unittest.TestLoader().loadTestsFromTestCase(TestExtractionFunctions)
     unittest.TextTestRunner(verbosity=2).run(TESTS)
diff --git a/tests/test_simplify.py b/tests/test_simplify.py
new file mode 100644
index 0000000..139e556
--- /dev/null
+++ b/tests/test_simplify.py
@@ -0,0 +1,115 @@
+"""
+This file is part of OSM-flex.
+Copyright (C) 2023 OSM-flex contributors listed in AUTHORS.
+OSM-flex is free software: you can redistribute it and/or modify it under the
+terms of the GNU General Public License as published by the Free
+Software Foundation, version 3.
+OSM-flex is distributed in the hope that it will be useful, but WITHOUT ANY
+WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+PARTICULAR PURPOSE.  See the GNU General Public License for more details.
+-----
+test simplification functions
+"""
+
+import unittest
+import geopandas as gpd
+import shapely as sh
+from osm_flex.simplify import (remove_small_polygons, remove_contained_points,
+                               remove_contained_polys, remove_exact_duplicates)
+from pandas.testing import assert_frame_equal
+from pathlib import Path
+
+PATH_TEST_DATA = Path(__file__).parent / 'data'
+OSM_FILE = PATH_TEST_DATA / 'test.osm.pbf'
+
+
+# Define some shapes
+coords1 = ((0., 0.), (0., 1.), (1., 1.), (1., 0.), (0., 0.)) #area=1
+coords2 = ((0., 0.), (0., 0.1), (0.1, 0.1), (0.1, 0.), (0., 0.)) #area=0.01
+polygon1 = sh.Polygon(coords1)
+polygon2 = sh.Polygon(coords2)
+point1 = sh.Point(1.0, -1.0)
+point2 = sh.Point(.5, .5)
+line1 = sh.LineString([[0, 0], [1, 0], [1, 1]])
+line2 = sh.LineString([[0.25, 0.25], [0.75, 0.75]])
+
+class TestSimplificationFunctions(unittest.TestCase):
+    def test_remove_small_polygons(self):
+        """ test function remove_small_polygons() """
+
+        gdf_small_poly = gpd.GeoDataFrame(
+            geometry = [
+                point1,
+                line1,
+                polygon1,
+                polygon2
+            ])
+
+        gdf_no_small_poly = gpd.GeoDataFrame(
+            geometry = [
+                point1,
+                line1,
+                polygon1,
+            ])
+
+        # get rid of polygon2
+        gdf_removed = remove_small_polygons(gdf_small_poly, 0.5)
+
+        assert_frame_equal(gdf_removed, gdf_no_small_poly)
+
+    def test_remove_contained_points(self):
+        """ test function remove_contained_points() """
+
+        gdf_pnt_in_poly = gpd.GeoDataFrame(
+            geometry = [polygon1, polygon2, point1, point2, line1
+            ])
+
+        gdf_check  = gpd.GeoDataFrame(
+            geometry = [polygon1, polygon2, point1,line1
+            ])
+
+        # get rid of point2
+        gdf_simple  = remove_contained_points(gdf_pnt_in_poly)
+
+        assert_frame_equal(gdf_check, gdf_simple)
+
+
+
+    def test_remove_contained_polys(self):
+        """ test function remove_contained_polys() """
+
+        gdf_poly_in_poly = gpd.GeoDataFrame(
+            geometry = [polygon1, polygon2, point1, point2, line1, line2
+            ])
+
+        gdf_check  = gpd.GeoDataFrame(
+            geometry = [polygon1, point1, point2, line1, line2
+            ])
+
+        # get rid of polygon2
+        gdf_simple = remove_contained_polys(gdf_poly_in_poly)
+
+        assert_frame_equal(gdf_check, gdf_simple)
+
+
+
+    def test_remove_exact_duplicates(self):
+        """ test function remove_exact_duplicates() """
+
+        gdf_dupl = gpd.GeoDataFrame(
+            geometry = [polygon1, polygon1, point1, point1, line1, line1
+            ])
+
+        # get rid of polygon1, point1, line1
+        gdf_simple = remove_exact_duplicates(gdf_dupl)
+
+        gdf_check  = gpd.GeoDataFrame(
+            geometry = [polygon1, point1, line1
+            ])
+
+        assert_frame_equal(gdf_check, gdf_simple)
+
+
+if __name__ == "__main__":
+    TESTS = unittest.TestLoader().loadTestsFromTestCase(TestSimplificationFunctions)
+    unittest.TextTestRunner(verbosity=2).run(TESTS)
\ No newline at end of file