geospatialsuite0.1.1 package

Comprehensive Geospatiotemporal Analysis and Multimodal Integration Toolkit

add_boundary_overlay

Add boundary overlay to existing plot

add_crop_statistics_overlay

Add crop statistics overlay

add_lines_to_leaflet

Add lines to leaflet -

add_points_to_leaflet

Add points to leaflet -

add_polygons_to_leaflet

Add polygons to leaflet -

analyze_cdl_crops_dynamic

Analyze CDL crops dynamically

analyze_crop_vegetation

Specialized crop vegetation analysis

analyze_growth_stage

Analyze growth stage

analyze_seasonal_patterns

Analyze seasonal patterns

analyze_temporal_changes

Analyze temporal changes in geospatial data

analyze_variable_correlations

Analyze correlations between multiple variables

analyze_water_bodies

Analyze water body characteristics using multiple indices

analyze_water_quality_comprehensive

Analyze water quality comprehensively with flexible data handling

analyze_water_spatial_patterns

Analyze spatial patterns in water quality data

analyze_water_temporal_patterns

Analyze temporal patterns in water quality data

analyze_yield_potential

Analyze yield potential

apply_color_scheme_ggplot

Apply color scheme to ggplot

apply_quality_filter

Apply quality filter to remove outliers

apply_temporal_smoothing

Apply temporal smoothing

apply_water_quality_filters

Apply water quality filters

assess_data_for_interpolation

Assess data characteristics for interpolation method selection

auto_detect_method

Auto-detect best spatial join method

auto_detect_spectral_bands

Auto-detect spectral bands

auto_detect_title

Auto-detect title for quick mapping

auto_detect_variable

Auto-detect variable for quick mapping

auto_geocode_data

Auto-geocode data with geographic identifiers

calculate_advanced_terrain_metrics

Calculate advanced terrain metrics

calculate_comprehensive_vegetation_stats

Calculate comprehensive vegetation statistics -

calculate_contributing_area

Calculate contributing area

calculate_index_by_type

Calculate index by type

calculate_multiple_indices

Calculate multiple vegetation indices at once

calculate_multiple_water_indices

Calculate multiple water indices at once

calculate_ndvi_enhanced

Calculate NDVI with time series options

calculate_skewness

Calculate skewness for data assessment

calculate_spatial_correlation

Calculate spatial correlation between raster layers

calculate_temporal_statistics

Calculate temporal statistics

calculate_temporal_trend

Calculate temporal trend using linear regression

calculate_vegetation_index

Calculate comprehensive vegetation indices

calculate_vegetation_statistics

Calculate vegetation statistics

calculate_water_index

Calculate water indices including both NDWI variants

calculate_water_quality_statistics

Calculate comprehensive water quality statistics

check_raster_compatibility

Check raster compatibility

check_required_packages

Check and load required packages

classify_data_input

Classify data input type

classify_spatial_data

Classify spatial data type and load data

combine_water_datasets

Combine multiple water quality datasets

compare_interpolation_methods

Compare interpolation methods

compile_interpolation_results

Compile interpolation results into requested format

convert_julian_date

Convert Julian date to standard format

convert_to_spatial_flexible

Convert data.frame to sf with flexible coordinate detection

create_base_plot_map

Create base plot map as fallback

create_comparison_map

Create comparison map (before/after, side-by-side)

create_correlation_plots

Create correlation plots

create_crop_area_map

Create crop area map -

create_crop_diversity_map

Create crop diversity map -

create_crop_map

Create crop map visualization

create_crop_mask

Create crop mask from CDL data

create_dominant_crop_map

Create dominant crop map -

create_ggplot_map_safe

Create ggplot map safely

create_interactive_map_safe

Create interactive map safely

create_interactive_map

Create interactive map using leaflet (if available)

create_ndvi_map

Create NDVI map visualization

create_overlay_comparison

Create overlay comparison

create_raster_map_reliable

Create reliable raster map

create_raster_mosaic

Create raster mosaic with intelligent file selection

create_spatial_map

Create universal spatial map with reliable terra plotting

create_water_quality_plot_robust

Create robust water quality visualization

create_water_quality_plot

Create water quality plot

detect_coordinate_columns

Detect coordinate columns automatically

detect_geographic_entities

Detect and geocode geographic entity columns

detect_geometry_type

Detect geometry type automatically

detect_huc_level

Detect HUC level from column name or data

detect_state_column

Helper to detect state column

detect_temporal_changes

Detect temporal changes between periods

detect_vegetation_stress

Detect vegetation stress

detect_water_quality_columns

Detect water quality data columns intelligently

execute_interpolation_method

Execute specific interpolation method

execute_mice_interpolation

Execute MICE interpolation

execute_nn_interpolation

Execute nearest neighbor interpolation

execute_simple_interpolation

Execute simple distance weighting interpolation

execute_spline_interpolation

Execute spline interpolation

extract_aster_coordinates

Extract coordinates from ASTER filename

extract_bands_from_raster

Extract bands from multi-band raster

extract_dates_universal

Extract dates from filenames using various patterns

generate_enhanced_analysis_summary

Generate enhanced analysis summary

generate_html_summary

Generate HTML summary report

generate_statistics_report

Generate comprehensive statistics report

geocode_cities

Geocode city names

geocode_counties

Geocode US counties

geocode_entities

Geocode geographic entities to coordinates

geocode_fips

Geocode FIPS codes

geocode_hucs

Geocode HUC watershed codes

geocode_states

Geocode US states

geocode_zipcodes

Geocode ZIP codes

geocoding_examples

Geocoding Examples and Use Cases

geospatialsuite-package

geospatialsuite: Comprehensive Geospatiotemporal Analysis and Multimod...

get_available_indices

Get available indices

get_comprehensive_cdl_codes

Get comprehensive CDL crop codes

get_index_formulas

Get index formulas

get_index_ranges

Get index typical ranges

get_index_references

Get index references

get_interpretation_guidelines

Get interpretation guidelines

get_region_boundary

Get region boundary for any specified region

get_reliable_colors

Get reliable colors for terra plotting

get_satellite_band_info

Get satellite band information

get_summary_function

Get summary function for terra operations

get_terra_colors

Get terra colors for plotting

get_water_index_formulas

Get water index formulas

get_water_index_requirements

Get water index requirements

handle_index_edge_cases

Handle edge cases for index calculations

handle_outliers_in_data

Handle outliers in spatial data

handle_water_index_edge_cases

Handle edge cases for water index calculations

integrate_multiple_datasets

Integrate multiple datasets

integrate_terrain_analysis

Integrate terrain analysis with vector data

is_valid_date

Validate date string

list_vegetation_indices

Get comprehensive list of available vegetation indices

list_water_indices

Get comprehensive list of available water indices

load_and_process_spatial_data

Load and process spatial data for interpolation

load_and_stack_bands

Load and stack individual band files

load_and_validate_band

Load and validate spectral band

load_bands_from_directory

Load bands from directory

load_raster_data

Load raster data from various sources

load_raster_safe

Load raster with error handling

load_single_water_dataset

Load single water quality dataset

load_vector_data_safe

Load vector data safely with coordinate detection

load_water_quality_data_flexible

Load water quality data with flexible format handling

map_custom_band_names

Map custom band names

mask_invalid_values

Mask invalid values based on index type

mask_invalid_water_values

Mask invalid values for water indices

match_rasters_by_date

Match rasters by date

multiscale_operations

Multi-scale spatial operations

normalize_column_name

Normalize column names for robust matching

perform_cross_validation

Perform cross-validation for interpolation accuracy

perform_extract_join

Perform extract join (vector to raster)

perform_overlay_join

Perform other join methods (stubs for now)

perform_resample_join

Perform resample join (raster to raster)

perform_threshold_analysis

Perform threshold analysis

plot_raster_fast

Create fast raster plot using terra

plot_rgb_raster

Create multi-band raster RGB plot

preview_geocoding

Preview geographic entity detection

process_spatial_data_safe

Process spatial data safely with error handling

process_vector_data

Process vector data from data frame

quick_diagnostic

Quick diagnostic check

quick_map

Quick map function - one-line mapping with auto-detection

raster_to_raster_ops

Raster to Raster Operations

run_comprehensive_geospatial_workflow

Run comprehensive geospatial workflow -

run_comprehensive_vegetation_workflow

Run comprehensive vegetation analysis workflow -

run_enhanced_mosaic_workflow

Run enhanced mosaic workflow

run_enhanced_ndvi_crop_workflow

Run enhanced NDVI crop analysis workflow -

run_enhanced_temporal_workflow

Run enhanced temporal workflow

run_enhanced_terrain_analysis_workflow

Run enhanced terrain analysis workflow

run_enhanced_water_quality_workflow

Run enhanced water quality analysis workflow

run_interactive_mapping_workflow

Run interactive mapping workflow

run_multi_dataset_workflow

Run multi-dataset workflow

save_interactive_map

Save interactive map to file

save_interpolation_results

Save interpolation results to file

save_plot_to_file

Save plot to file with error handling

save_static_map

Save static map with ggplot2

save_temporal_results

Save temporal analysis results

save_vegetation_analysis_results

Save vegetation analysis results

save_water_quality_results

Save water quality analysis results

select_crop_indices

Select appropriate indices for crop analysis

select_optimal_method

Select optimal interpolation method based on data characteristics

select_rasters_for_region

Select rasters for specific region with intelligent filtering

spatial_interpolation_comprehensive

Perform spatial interpolation for missing data

spatial_interpolation

Legacy spatial interpolation function (for backward compatibility)

test_function_availability

Test individual function existence

test_geospatialsuite_package_simple

Test GeoSpatialSuite with simplified, robust tests

test_package_minimal

Test package with minimal complexity

universal_spatial_join

Universal Spatial Join - Complete Implementation

validate_method_compatibility

Validate method compatibility

validate_method

Validate method parameter

validate_numeric_range

Validate numeric range

validate_output

Validate output quality

validate_raster_input

Validate raster input

validate_required_bands

Validate required bands for specific indices

validate_vector_input

Validate vector input

validate_vegetation_analysis

Validate vegetation analysis

validate_water_index_output

Validate water index output

validation-helpers

Input Validation Helpers

A comprehensive toolkit for geospatiotemporal analysis featuring 60+ vegetation indices, advanced raster visualization, universal spatial mapping, water quality analysis, CDL crop analysis, spatial interpolation, temporal analysis, and terrain analysis. Designed for agricultural research, environmental monitoring, remote sensing applications, and publication-quality mapping with support for any geographic region and robust error handling. Methods include vegetation indices calculations (Rouse et al. 1974), NDVI and enhanced vegetation indices (Huete et al. 1997) <doi:10.1016/S0034-4257(97)00104-1>, (Akanbi et al. 2024) <doi:10.1007/s41651-023-00164-y>, spatial interpolation techniques (Cressie 1993, ISBN:9780471002556), water quality indices (McFeeters 1996) <doi:10.1080/01431169608948714>, and crop data layer analysis (USDA NASS 2024) <https://www.nass.usda.gov/Research_and_Science/Cropland/>. Funding: This material is based upon financial support by the National Science Foundation, EEC Division of Engineering Education and Centers, NSF Engineering Research Center for Advancing Sustainable and Distributed Fertilizer production (CASFER), NSF 20-553 Gen-4 Engineering Research Centers award 2133576.

  • Maintainer: Olatunde D. Akanbi
  • License: MIT + file LICENSE
  • Last published: 2025-11-05