How can you remove outliers from geospatial data?

1 Identify outliers

The first step is to identify outliers in your geospatial data. There are different ways to do this, depending on the type and format of your data. For example, you can use descriptive statistics, such as mean, median, standard deviation, and quartiles, to calculate the range and distribution of your data values. You can also use graphical methods, such as box plots, histograms, scatter plots, and maps, to visually inspect your data and spot any anomalies. You can also use domain knowledge, or the understanding of the context and meaning of your data, to determine if some values are unrealistic or erroneous.

2 Remove outliers

The second step is to remove outliers from your geospatial data. There are different ways to do this, depending on the purpose and impact of your data analysis and visualization. For example, you can simply delete the outliers, or replace them with missing values, if they are clearly incorrect or irrelevant for your research question. You can also use trimming, or cutting off the extreme values from your data set, if you want to focus on the central tendency of your data. You can also use winsorizing, or replacing the extreme values with the nearest non-outlier values, if you want to reduce the skewness and variance of your data. You can also use transformation, or applying a mathematical function to your data, such as log or square root, if you want to normalize your data and make it more suitable for certain types of analysis and visualization.

3 Validate results

The third step is to validate the results of your outlier removal process. There are different ways to do this, depending on the criteria and measures of your data quality and accuracy. For example, you can use descriptive statistics, such as mean, median, standard deviation, and quartiles, to compare the summary and distribution of your original and cleaned data sets. You can also use graphical methods, such as box plots, histograms, scatter plots, and maps, to compare the visual and spatial patterns of your original and cleaned data sets. You can also use inferential statistics, such as hypothesis testing and confidence intervals, to compare the significance and reliability of your original and cleaned data sets.

4 Choose appropriate visualization

The fourth step is to choose an appropriate visualization for your geospatial data. There are different types of visualization, depending on the variables and dimensions of your data. For example, you can use point maps, or maps that show individual locations or events, if you want to display the distribution and density of your data. You can also use choropleth maps, or maps that show aggregated values by regions or areas, if you want to display the variation and comparison of your data. You can also use heat maps, or maps that show the intensity of values by colors, if you want to display the concentration and gradient of your data.

5 Enhance readability

The fifth step is to enhance the readability of your geospatial data visualization. There are different ways to do this, depending on the design and presentation of your visualization. For example, you can use color, size, shape, and symbols to encode and emphasize your data values and categories. You can also use labels, legends, titles, and annotations to explain and clarify your data variables and dimensions. You can also use scales, axes, grids, and basemaps to provide context and reference for your data coordinates and locations.

6 Communicate insights

The sixth and final step is to communicate the insights from your geospatial data visualization. There are different ways to do this, depending on the audience and purpose of your visualization. For example, you can use stories, narratives, and scenarios to engage and persuade your audience with your data findings and recommendations. You can also use charts, tables, and numbers to support and validate your data claims and arguments. You can also use interactivity, animation, and exploration to invite and involve your audience in your data analysis and visualization.