1+ {
2+ "cells" : [
3+ {
4+ "cell_type" : " markdown"
5+ "id" : " 7fb27b941602401d91542211134fc71a"
6+ "metadata" : {},
7+ "source" : [
8+ " # Kentucky County Data Explorer\n "
9+ " \n "
10+ " Explore KyFromAbove data availability across Kentucky's 120 counties.\n "
11+ " This notebook demonstrates how to:\n "
12+ " - List all available counties\n "
13+ " - Search multiple products for a single county\n "
14+ " - Compare tile coverage across products\n "
15+ " - Export results to GeoJSON"
16+ ]
17+ },
18+ {
19+ "cell_type" : " code"
20+ "execution_count" : null ,
21+ "id" : " acae54e37e7d407bbb7b55eff062a284"
22+ "metadata" : {},
23+ "outputs" : [],
24+ "source" : [
25+ " import matplotlib.pyplot as plt\n "
26+ " \n "
27+ " import abovepy\n "
28+ " from abovepy.utils.bbox import get_county_bbox, list_counties"
29+ ]
30+ },
31+ {
32+ "cell_type" : " markdown"
33+ "id" : " 9a63283cbaf04dbcab1f6479b197f3a8"
34+ "metadata" : {},
35+ "source" : [
36+ " ## List All Counties\n "
37+ " \n "
38+ " abovepy ships with bounding boxes for all 120 Kentucky counties.\n "
39+ " These are used internally when you pass `county=` to `abovepy.search()`."
40+ ]
41+ },
42+ {
43+ "cell_type" : " code"
44+ "execution_count" : null ,
45+ "id" : " 8dd0d8092fe74a7c96281538738b07e2"
46+ "metadata" : {},
47+ "outputs" : [],
48+ "source" : [
49+ " counties = list_counties()\n "
50+ " print(f\" Total counties: {len(counties)}\\ n\" )\n "
51+ " \n "
52+ " # Display in columns for readability\n "
53+ " cols = 4\n "
54+ " for i in range(0, len(counties), cols):\n "
55+ " row = counties[i:i + cols]\n "
56+ " print(\" \" .join(f\" {c:<16}\" for c in row))"
57+ ]
58+ },
59+ {
60+ "cell_type" : " markdown"
61+ "id" : " 72eea5119410473aa328ad9291626812"
62+ "metadata" : {},
63+ "source" : [
64+ " ## Search Multiple Products for One County\n "
65+ " \n "
66+ " Let's see what data is available for Pike County — the largest county by area\n "
67+ " in eastern Kentucky. We'll search across DEM, orthoimagery, and LiDAR products."
68+ ]
69+ },
70+ {
71+ "cell_type" : " code"
72+ "execution_count" : null ,
73+ "id" : " 8edb47106e1a46a883d545849b8ab81b"
74+ "metadata" : {},
75+ "outputs" : [],
76+ "source" : [
77+ " county_name = \" Pike\"\n "
78+ " print(f\" Searching {county_name} County...\" )\n "
79+ " print(f\" Bounding box: {get_county_bbox(county_name)}\\ n\" )\n "
80+ " \n "
81+ " products_to_search = [\n "
82+ " \" dem_phase1\" ,\n "
83+ " \" dem_phase2\" ,\n "
84+ " \" dem_phase3\" ,\n "
85+ " \" ortho_phase1\" ,\n "
86+ " \" ortho_phase2\" ,\n "
87+ " \" ortho_phase3\" ,\n "
88+ " \" laz_phase1\" ,\n "
89+ " \" laz_phase2\" ,\n "
90+ " \" laz_phase3\" ,\n "
91+ " ]\n "
92+ " \n "
93+ " results = {}\n "
94+ " for product in products_to_search:\n "
95+ " try:\n "
96+ " tiles = abovepy.search(county=county_name, product=product)\n "
97+ " results[product] = tiles\n "
98+ " print(f\" {product:<16} {len(tiles):>5} tiles\" )\n "
99+ " except Exception as e:\n "
100+ " print(f\" {product:<16} Error: {e}\" )\n "
101+ " \n "
102+ " print(f\"\\ nTotal products with data: {len(results)}\" )"
103+ ]
104+ },
105+ {
106+ "cell_type" : " markdown"
107+ "id" : " 10185d26023b46108eb7d9f57d49d2b3"
108+ "metadata" : {},
109+ "source" : [
110+ " ## Compare Tile Coverage\n "
111+ " \n "
112+ " Visualize the spatial coverage of different products over Pike County\n "
113+ " by plotting the tile boundaries from each search result."
114+ ]
115+ },
116+ {
117+ "cell_type" : " code"
118+ "execution_count" : null ,
119+ "id" : " 8763a12b2bbd4a93a75aff182afb95dc"
120+ "metadata" : {},
121+ "outputs" : [],
122+ "source" : [
123+ " # Pick up to 3 products that returned results for comparison\n "
124+ " products_to_plot = [k for k in [\" dem_phase3\" , \" ortho_phase3\" , \" laz_phase3\" ] if k in results]\n "
125+ " \n "
126+ " if products_to_plot:\n "
127+ " fig, axes = plt.subplots(\n "
128+ " 1, len(products_to_plot),\n "
129+ " figsize=(6 * len(products_to_plot), 6),\n "
130+ " squeeze=False,\n "
131+ " )\n "
132+ " \n "
133+ " for idx, product in enumerate(products_to_plot):\n "
134+ " ax = axes[0][idx]\n "
135+ " gdf = results[product]\n "
136+ " gdf.plot(ax=ax, edgecolor=\" steelblue\" , facecolor=\" lightblue\" , alpha=0.5, linewidth=0.5)\n "
137+ " ax.set_title(f\" {product}\\ n({len(gdf)} tiles)\" )\n "
138+ " ax.set_xlabel(\" Easting\" )\n "
139+ " ax.set_ylabel(\" Northing\" )\n "
140+ " \n "
141+ " plt.suptitle(f\" {county_name} County — Tile Coverage by Product\" , fontsize=14, y=1.02)\n "
142+ " plt.tight_layout()\n "
143+ " plt.show()\n "
144+ " else:\n "
145+ " print(\" No products returned results for plotting.\" )"
146+ ]
147+ },
148+ {
149+ "cell_type" : " markdown"
150+ "id" : " 7623eae2785240b9bd12b16a66d81610"
151+ "metadata" : {},
152+ "source" : [
153+ " ## Export Results\n "
154+ " \n "
155+ " Export the tile index for a product to GeoJSON for use in other tools\n "
156+ " (QGIS, ArcGIS Pro, web maps, etc.)."
157+ ]
158+ },
159+ {
160+ "cell_type" : " code"
161+ "execution_count" : null ,
162+ "id" : " 7cdc8c89c7104fffa095e18ddfef8986"
163+ "metadata" : {},
164+ "outputs" : [],
165+ "source" : [
166+ " from pathlib import Path\n "
167+ " \n "
168+ " output_dir = Path(\" ./output\" )\n "
169+ " output_dir.mkdir(exist_ok=True)\n "
170+ " \n "
171+ " # Export each product's tile index to GeoJSON\n "
172+ " for product, gdf in results.items():\n "
173+ " output_path = output_dir / f\" {county_name.lower()}_{product}.geojson\"\n "
174+ " gdf.to_file(output_path, driver=\" GeoJSON\" )\n "
175+ " print(f\" Exported {product}: {output_path} ({len(gdf)} tiles)\" )\n "
176+ " \n "
177+ " print(f\"\\ nAll exports saved to {output_dir.resolve()}\" )"
178+ ]
179+ }
180+ ],
181+ "metadata" : {
182+ "kernelspec" : {
183+ "display_name" : " Python 3"
184+ "language" : " python"
185+ "name" : " python3"
186+ },
187+ "language_info" : {
188+ "name" : " python"
189+ "version" : " 3.10.0"
190+ }
191+ },
192+ "nbformat" : 4 ,
193+ "nbformat_minor" : 5
194+ }
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