3.3. Lesson: 分類

Labels are a good way to communicate information such as the names of individual places, but they can't be used for everything. For example, let us say that someone wants to know what each landuse area is used for. Using labels, you would get this:

../../../_images/bad_landuse_labels.png

これでは地図の標識が読みにくくなり、地図上に多数の異なる土地利用の領域がある場合は圧倒的にさえなります。

このレッスンの目標: 効果的にベクターデータを分類する方法を学習します。

3.3.1. basic Follow Along: 名義データを分類する

  1. Open the Layer Properties dialog for the landuse layer

  2. Go to the Symbology tab

  3. 単一シンボル と言うドロップダウンをクリックし、それを 分類された に変更します:

    ../../../_images/categorised_styles.png
  4. In the new panel, change the Value to landuse and the Color ramp to Random colors

  5. Click the button labeled Classify

    ../../../_images/categorised_style_settings.png
  6. OK をクリックします

    このように表示されるはずです:

    ../../../_images/categorisation_result.png
  7. Click the arrow (or plus sign) next to landuse in the Layers panel, you'll see the categories explained:

    ../../../_images/categories_explained.png

    Now our landuse polygons are colored and are classified so that areas with the same land use are the same color.

  8. If you wish to, you can change the symbol of each landuse area by double-clicking the relevant color block in the Layers panel or in the Layer Properties dialog:

    ../../../_images/change_layer_color.png

空の分類が一つあることに注意してください:

../../../_images/empty_category.png

This empty category is used to color any objects which do not have a landuse value defined or which have a NULL value. It can be useful to keep this empty category so that areas with a NULL value are still represented on the map. You may like to change the color to more obviously represent a blank or NULL value.

すべての苦労して稼いだ変更を失わないように、今、地図を保存することを忘れないでください!

3.3.2. basic Try Yourself その他の分類

Use the knowledge you gained above to classify the buildings layer. Set the categorisation against the building field and use the Spectral color ramp.

注釈

忘れずに、都市エリアを拡大して結果を確認してください。

3.3.3. moderate Follow Along: 比率分類

分類には4つのタイプがあります: 名義順序間隔比率

In nominal classification, the categories that objects are classified into are name-based; they have no order. For example: town names, district codes, etc. Symbols that are used for nominal data should not imply any order or magnitude.

  • For points, we can use symbols of different shape.

  • For polygons, we can use different types of hatching or different colours (avoid mixing light and dark colours).

  • For lines, we can use different dash patterns, different colours (avoid mixing light and dark colours) and different symbols along the lines.

In ordinal classification, the categories are arranged in a certain order. For example, world cities are given a rank depending on their importance for world trade, travel, culture, etc. Symbols that are used for ordinal data should imply order, but not magnitude.

  • For points, we can use symbols with light to dark colours.

  • For polygons, we can use graduated colours (light to dark).

  • For lines, we can use graduated colours (light to dark).

In interval classification, the numbers are on a scale with positive, negative and zero values. For example: height above/below sea level, temperature in degrees Celsius. Symbols that are used for ratio data should imply order and magnitude.

  • For points, we can use symbols with varying size (small to big).

  • For polygons, we can use graduated colours (light to dark) or add diagrams of varying size.

  • For lines, we can use thickness (thin to thick).

In ratio classification, the numbers are on a scale with only positive and zero values. For example: temperature above absolute zero (0 degrees Kelvin), distance from a point, the average amount of traffic on a given street per month, etc. Symbols that are used for ratio data should imply order and magnitude.

  • For points, we can use symbols with varying size (small to big).

  • For polygons, we can use graduated colours (light to dark) or add diagrams of varying size.

  • For lines, we can use thickness (thin to thick).

In the example above, we used nominal classification to color each record in the landuse layer based on its landuse attribute. Now we will use ratio classification to classify the records by area.

We are going to reclassify the layer, so existing classes will be lost if not saved. To store the current classification:

  1. Open the layer's properties dialog

  2. Click the Save Style ... button in the Style drop-down menu.

  3. Select Rename Current..., enter land usage and press OK.

    The categories and their symbols are now saved in the layer's properties.

  4. Click now on the Add... entry of the Style drop-down menu and create a new style named ratio. This will store the new classification.

  5. Close the Layer Properties dialog

We want to classify the landuse areas by size, but there is a problem: they don't have a size field, so we'll have to make one.

  1. Open the Attributes Table for the landuse layer.

  2. Enter edit mode by clicking the toggleEditing Toggle editing button

  3. Add a new column of decimal type, called AREA, using the newAttribute New field button:

    ../../../_images/add_area_column.png
  4. OK をクリックします

    The new field will be added (at the far right of the table; you may need to scroll horizontally to see it). However, at the moment it is not populated, it just has a lot of NULL values.

    To solve this problem, we will need to calculate the areas.

    1. Open the field calculator with the calculateField button.

      You will get this dialog:

      ../../../_images/calculate_field_dialog.png
    2. Check the checkbox Update existing fields

    3. Select AREA in the fields drop-down menu

      ../../../_images/field_calculator_top.png
    4. Under the Expression tab, expand the Geometry functions group in the list and find $area

    5. Double-click on it so that it appears in the Expression field

      ../../../_images/geometry_area_select.png
    6. OK をクリックします

    7. Scroll to the AREA field in the attribute table and you will notice that it is populated with values (you may need to click the column header to refresh the data).

    注釈

    These areas respect the project's area unit settings, so they may be in square meters or square degrees.

  5. Press saveEdits to save the edits and exit the edit mode with toggleEditing Toggle editing

  6. Close the attribute table

Now that we have the data, let's use them to render the landuse layer.

  1. Open the Layer properties dialog's Symbology tab for the landuse layer

  2. Change the classification style from Categorized to Graduated

  3. Change the Value to AREA

  4. Under Color ramp, choose the option Create New Color Ramp...:

    ../../../_images/area_gradient_select.png
  5. Choose Gradient (if it's not selected already) and click OK. You will see this:

    ../../../_images/gradient_color_select.png

    これを使用して、小さな面積の地域は 1 の色、大きな面積 2 の色で表示します。

  6. Choose appropriate colors

    例では、結果は次のようになります。

    ../../../_images/gradient_color_example.png
  7. OK をクリックします

  8. You can save the colour ramp by selecting Save Color Ramp... under the Color ramp tab. Choose an appropriate name for the colour ramp and click Save. You will now be able to select the same colour ramp easily under All Color Ramps.

  9. Click Classify

    Now you will have something like this:

    ../../../_images/landuse_gradient_selected.png

    そのまま他のすべてを残します。

  10. Click OK:

../../../_images/gradient_result_map.png

3.3.4. moderate Try Yourself 分類の絞り込み

  • 理にかなった分類を得るまで モードクラス の値を変更します。

結果を確認します

3.3.5. hard Follow Along: 規則に基づく分類

分類のための複数の条件を組み合わせると便利ですが、残念ながら通常の分類では1つの属性だけを考慮に入れます。規則に基づく分類が便利になるところです。

In this lesson, we will represent the landuse layer in a way to easily identify Swellendam city from the other residential area, and from the other types of landuse (based on their area).

  1. Open the Layer Properties dialog for the landuse layer

  2. Switch to the Symbology tab

  3. Switch the classification style to Rule-based

    QGIS will automatically show the rules that represent the current classification implemented for this layer. For example, after completing the exercise above, you may see something like this:

    ../../../_images/rule_based_classification.png
  4. Click and drag to select all the rules

  5. Use the signMinus Remove selected rules button to remove all of the existing rules

Let's now add our custom rules.

  1. Click the signPlus Add rule button

  2. The Edit rule dialog then appears

  3. Enter Swellendam city as Label

  4. Click the expression button next to the Filter text area to open the Expression String Builder

  5. Enter the criterion "name" = 'Swellendam' and validate

    ../../../_images/query_builder_example.png
  6. Back to the Edit rule dalog, assign it a darker grey-blue color in order to indicate the town's importance in the region and remove the border

    ../../../_images/rule_style_result.png
  7. Press OK

  8. Repeat the steps above to add the following rules:

    1. Other residential label with the criterion "landuse" = 'residential' AND "name" <> 'Swellendam' (or "landuse" = 'residential' AND "name" != 'Swellendam'). Choose a pale blue-grey Fill color

    2. Big non residential areas label with the criterion "landuse" <> 'residential' AND "AREA" >= 605000. Choose a mid-green color.

      ../../../_images/criterion_refined_midway.png

      These filters are exclusive, in that they exclude areas on the map (non-residential areas which are smaller than 605000 (square meters) are not included in any of the rules).

    3. We will catch the remaining features using a new rule labeled Small non residential areas. Instead of a filter expression, Check the radioButtonOn Else. Give this category a suitable pale green color.

      ../../../_images/criterion_else.png

    Your rules should now look like this:

    ../../../_images/criterion_refined_list.png
  9. Apply this symbology

地図は次のようになります。

../../../_images/rule_based_map_result.png

今、 最も顕著な住宅地とその大きさに応じて色の他の非住宅地 Swellendam で地図を持っています。

3.3.6. In Conclusion

シンボル体系により、レイヤーの属性は読みとりやすく表現できます。それは選択した任意の関連する属性を使用して、私たちだけでなく地図読者も地物の重要性を理解できます。直面している問題に応じて、それらを解決するために異なる分類技術を適用するでしょう。

3.3.7. What's Next?

これで見栄えの良い地図になりましたが、どのようにしてそれをQGISからプリントアウトできる形式に、または画像やPDFにしようとしていますか?それは、次のレッスンの話題です!