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Abstract
lors, and no axes by default, streamlining the process of plotting vector data. If needed, you can easily customize the appearance using gspatial_plot, Geopandas, or Matplotlib parameters.</p><div id="7f83"><pre>gsp<span class="hljs-selector-class">.shapeplot</span>(usa, figsize=(<span class="hljs-number">15</span>, <span class="hljs-number">15</span>))</pre></div><figure id="5a4f"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*hgepISp4blKLRyoMFCcqAg.png"><figcaption></figcaption></figure><p id="970d">You also have the option to adjust the parameters according to your needs. For instance, let’s incorporate a title into the visualization:</p><div id="b7f0"><pre>gsp.shapeplot(
usa,
title=<span class="hljs-string">"USA MAP"</span>,
title_kwds={<span class="hljs-string">"fontsize"</span>: 25, <span class="hljs-string">"fontname"</span>: <span class="hljs-string">"sans-serif"</span>, <span class="hljs-string">"fontweight"</span>: 3},
)</pre></div><figure id="4438"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*mIWUUXOP3u1uiZXQs_N2Lg.png"><figcaption></figcaption></figure><p id="1dc8"><b>Here’s the explanation of the shapeplot function’s parameters:</b></p><p id="7d22">• data (GeoDataFrame): GeoDataFrame used as the data source for the map.
• title (str): Title displayed on the map. Default is None.
• title_kwds (dict): Keyword arguments for configuring title appearance using matplotlib.pyplot.title. Default is an empty dictionary.
• figsize (tuple): Dimensions of the figure. Default is (15, 15).
• facecolor (str): Background color of the figure. Default is “white”.
• edgecolor (str): Color of the map’s edges. Default is “black”.
• linewidth (float): Line width for shapes. Default is 0.5.
• color (str): Color of the shape. Default is “#F1F3F4”.
• annot (bool): Toggle annotations on/off. Default is False.
• annot_column (str/GeoDataFrame column): Source column for annotations. Default is None.
• annot_align (str): Text alignment for annotations. Default is “center”.
• annot_kwds (dict): Keyword arguments for customizing annotations. Default is an empty dictionary.
• ax (matplotlib axis): Existing axis for plotting, if needed. Default is None.
• axis_on (bool): Toggle axis visibility. Default is False.
• **geopandas_plot_kwds: Additional keyword arguments for Geopandas plot customization.</p><p id="5a62">Returns: Matplotlib axis object (ax) representing the map.</p><p id="39a4">This function enables straightforward GeoDataFrame shape plotting with customizable options for appearance and annotations, while allowing flexibility in using existing axes for plotting. The function returns a matplotlib axis object representing the generated map.</p><h1 id="7308">pointplot</h1><p id="e010">Point plot is designed specifically for visualizing point data. A key distinction between point plot and shapeplot lies in the ability to utilize a base vector layer of shapes. This base layer allows points to be plotted on top, enabling a layered representation.</p><div id="62be"><pre>usa_points = usa.representative_point()
gsp.pointplot(usa_points, <span class="hljs-keyword">base</span>=usa)</pre></div><figure id="e5d7"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*ZOiYi_T2DL9CU9Kx7-KpGA.png"><figcaption></figcaption></figure><p id="6b86">Alternatively, you have the option to plot the polygons themselves instead of just their boundaries in the base layer.</p><div id="aac0"><pre>gsp.pointplot(usa_points, <span class="hljs-keyword">base</span>=usa, base_boundary=False)</pre></div><figure id="3412"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*_Sz_IGkazfobhvTpV3Y8Ag.png"><figcaption></figcaption></figure><p id="8f2e">Points can be plotted independently, without the need for a base layer.</p><div id="30aa"><pre>gsp<span class="hljs-selector-class">.pointplot</span>(usa_points)</pre></div><figure id="d8ed"><img src="https://cdn
Options
-images-1.readmedium.com/v2/resize:fit:800/1*zFOL9eUsmS-5S67MeufNcQ.png"><figcaption></figcaption></figure><p id="5c42">Matplotlib axis objects offer the ability to merge and combine multiple plots.</p><div id="4753"><pre>ax = gsp.shapeplot(usa, figsize=(<span class="hljs-number">15</span>, <span class="hljs-number">15</span>))
gsp.pointplot(usa_points, ax=ax)</pre></div><figure id="f05f"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*_Sz_IGkazfobhvTpV3Y8Ag.png"><figcaption></figcaption></figure><p id="b9e0">It’s also possible to tailor the appearance of the base layer according to your preferences.</p><div id="3dfa"><pre>gsp.pointplot(
usa_points,
base=usa,
basecolor=<span class="hljs-string">"#7aebff"</span>,
base_boundary=<span class="hljs-literal">False</span>,
title=<span class="hljs-string">"USA Points"</span>,
title_kwds={<span class="hljs-string">"fontsize"</span>: <span class="hljs-number">25</span>, <span class="hljs-string">"fontname"</span>: <span class="hljs-string">"sans-serif"</span>, <span class="hljs-string">"fontweight"</span>: <span class="hljs-number">3</span>},
)</pre></div><figure id="ce5a"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*AiPv5roDBIrvCc92oIvZ4g.png"><figcaption></figcaption></figure><p id="3d19"><b>Here’s the breakdown of the pointplot function’s parameters:</b></p><p id="a875">• data (GeoDataFrame): The GeoDataFrame used for plotting the map.
• base (GeoDataFrame): Base GeoDataFrame on top of which the data will be plotted. Defaults to None.
• title (str): Title of the map. Defaults to None.
• title_kwds (dict): Keyword arguments for configuring the appearance of the title using matplotlib.pyplot.title. Defaults to an empty dictionary.
• figsize (tuple): Size of the figure. Defaults to (15, 15).
• color (str): Color of the point. Defaults to “#ffb536”.
• edgecolor (str): Color of the map’s edges. Defaults to “black”.
• basecolor (str): Color of the base data. Defaults to “#F1F3F4”.
• baseboundarycolor (str): Boundary color of the base data. Defaults to “black”.
• base_boundary (bool): Toggle visibility of base data boundaries. Defaults to True.
• boundary_linewidth (float): Linewidth of the base data boundaries. Defaults to 0.5.
• linewidth (float): Width of lines for shapes. Defaults to 0.5.
• annot (bool): If True, annotations are generated. Defaults to False.
• annot_column (str/GeoDataFrame column): If annot is True, column should be passed as the source for annotation. Defaults to None.
• annot_align (str): Text alignment for annotations. Defaults to “center”.
• annot_kwds (dict): Keyword arguments for annotation customization. Defaults to an empty dictionary.
• ax (matplotlib axis): Existing axis for plotting. Defaults to None.
• axis_on (bool): Toggle axis visibility. Defaults to False.
• facecolor (str): Figure’s face color. Defaults to “white”.
• **geopandas_plot_kwds: Additional Geopandas plot keyword arguments.</p><p id="fd44">Returns: Matplotlib axis object (ax).</p><h1 id="44de">Tips for Polished Maps</h1><p id="46bb"><b>Harmonious Color Choices:</b>
Experiment with color palettes to create maps that are visually pleasing and easy to understand. Choose colors that enhance the readability of your map.</p><p id="6be4"><b>Precise Annotations:</b>
Annotations provide context to your maps. Customize annotation alignment and appearance to guide viewers through your spatial narrative seamlessly.</p><p id="b235"><b>Seamless Matplotlib Integration:</b>
Leverage the integration with Matplotlib to fine-tune your maps’ aesthetics, titles, and labels for a polished final product.</p><h1 id="071e">Conclusion</h1><p id="3df6">In this tutorial, we’ve covered the essentials of crafting geospatial maps using the gspatial_plot library. With gspatial_plot, the process of geospatial mapping becomes more manageable and easy. See you in the next tutorial— happy mapping!</p></article></body>
