Toolbox II: Spatial Analysis and Modelling Methods for Urban Health
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For a more interactive and comprehensive exploration, refer to this page. There, you will find databases enriched with various visual components including gallery views, table layouts, and boards, facilitating an enhanced understanding of the information presented.
| Name | Data | Category | Short Description | Suggested tools | Variable |
|---|---|---|---|---|---|
| Choropleth Mapping | Geospatial data (vector or raster) of health variables | Spatial Visualisation | Choropleth Mapping helps to understand and analyze the spatial distribution and patterns of health-related determinates and outcomes within urban areas. | GeodaPythonQGIS | Univariate |
| Bivariate Mapping | Spatial Visualisation | Bivariate mapping is a visualisation technique used it to visually represent two different variables on a single map. | PythonR | ||
| Heat Maps (Kernel Density Estimation) | Point-based data | Spatial Visualisation | Heat maps for urban health are visual representations that use color-coded gradients to depict variations in health-related indicators across different areas within a city. | GeodaQGIS | Univariate |
| Spatial Autocorrelation | Spatial Analysis | Spatial autocorrelation analysis can be used in urban health research to examine the presence and characteristics of spatial clustering or spatial dependence in health data. | |||
| Hot Spots Analysis | Spatial Analysis | Hotspot identification is used for identifying areas within an urban environment that have a higher concentration of health issues or disparities compared to surrounding areas. It apply | GeodaPythonQGIS | BivariableMultivariableUnivariate | |
| Accessibility Analysis | Geospatial data (vector or raster) of health variablesPoint-based data | Spatial Analysis | Accessibility Analysis evaluates the ease of reaching desired destinations or services, such as healthcare facilities or parks, within a geographic area. It considers factors such as distance, travel time, transportation networks, and the spatial distribution of resources. | ArcGISPythonQGISR | BivariableMultivariable |
| Spatial Interpolation | Spatial Analysis | Spatial interpolation is a technique used in urban health to estimate values of health-related variables or indicators at unsampled locations within an urban area. | |||
| Spatial Clustering and Regionalisation | PointPolygon | Spatial Analysis | Cluster analysis is a statistical method used in urban health research to identify distinct groups or clusters of areas based on their health characteristics. | GeodaPythonR | BivariableMultivariable |
| Classification and Regression Trees | A collection of variables related to urban health | Spatial Modelling | Decision Tree Regression and Classification, as a machine learning technique, is commonly used in urban health research to predict health-related outcomes or understand the importance of health-related variables | PythonRStata | Multivariable |
| Geographically Weighted Regression | Spatial Modelling | Geographically Weighted Regression is a spatial statistical technique used in urban health studies to examine the relationships between health outcomes and predictors while considering spatial variations. | GeodaStata | Multivariable | |
| Emotion Detection | Geotagged social media data | Emotion detection from geotagged social media data is to analyze and understand the emotions expressed by individuals in urban areas through their social media posts. | PythonR | Univariate | |
| Co-location Analysis | Spatial Analysis | Geoda | BivariableMultivariable | ||
| Semantic Segmentation | Semantic Segmentation is a computer vision task that is utilised to categorise each pixel in an image into a class or object. |