Docs/notebooks

Makefile

@@ -2,7 +2,7 @@
 all: build serve
 
 build:
-	jupyter-book build . --config docs/_config.yml --toc docs/_toc.yml
+	sphinx-build docs _build/html -b html
 
 serve:
 	open _build/html/index.html

docs/_toc.yml

@@ -25,6 +25,7 @@ parts:
   - caption: Notebook Examples
     numbered: False
     chapters:
-      - file: user-docs/space2stats_py_package_demo.ipynb
       - file: user-docs/space2stats_api_demo.ipynb
-      - file: user-docs/space2stats_api_demo_R.Rmd
+      - file: user-docs/space2stats_api_demo_R.md
+      - file: user-docs/space2stats_floods.ipynb
+      - file: user-docs/space2stats_py_package_demo.ipynb

docs/readme.md

@@ -245,7 +245,7 @@ The expected response is a JSON containing the hexagon ID and the requested fiel
 
 ### `/aggregate`
 
-The summary endpoint is very similar to the summary endpoint, but it returns an aggregate statistic for the entire area, based on an additional `aggregation type` function ('sum', 'avg', 'count', 'max' or 'min'). The request body is the same as the summary endpoint, with the addition of the `aggregation_type` field. 
+The aggregate endpoint is very similar to the summary endpoint, but it returns an aggregate statistic for the entire area, based on an additional `aggregation type` function ('sum', 'avg', 'count', 'max' or 'min'). The request body is the same as the summary endpoint, with the addition of the `aggregation_type` field. 
 
 This example uses an admin-1 province boundary from GeoBoundaries, retrieved as a `geopandas geodataframe` or `simple feature` (r).
 
@@ -352,9 +352,15 @@ The expected response is a JSON containing the requested aggregate statistic for
 {'sum_pop_2020': 1374175.833772784}
 ```
 
+## Notebook Examples
+
+- [**API Demo (Python)**](user-docs/space2stats_api_demo.ipynb)
+- [**API Demo (R)**](user-docs/space2stats_api_demo_R.md)
+- [**Exploring Flood Exposure (Python Functions)**](user-docs/space2stats_floods.ipynb)
+
 ## StatsTable Python Package
 
-In addition to the API, the `StatsTable` python package is being developed to enable faster database queries and scale research applications. The package currently supports a similar set of functions as the API (_fields_, _summaries_, and _aggregate_).
+In addition to the API, the `StatsTable` python package provides the API's underlying functionality as a set of functions (_fields_, _summaries_, and _aggregate_). The package enables researchers to work with the Space2Stats database directly and conduct faster queries and scale research applications.
 
 ```{note}
 This package is still under development. Currently, users need to set credential parameters to connect to the database. Reach out to [email protected] to request credentials.

docs/user-docs/space2stats_api_demo_R.Rmd

@@ -1,7 +1,10 @@
 ---
 title: "Space2Stats API Demo in R"
-output: html_notebook
+output:
+  html_document:
+    df_print: paged
 ---
+# R Example
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
@@ -47,6 +50,11 @@ print(unlist(available_fields))
 ## Define Area of Interest (AOI)
 
 ```{r}
+minx <- 29.038924
+miny <- -4.468958
+maxx <- 30.850461
+maxy <- -2.310523
+
 # Define Area of Interest (AOI) with NULL for properties to ensure it's treated as a valid dictionary
 aoi <- list(
   type = "Feature",
@@ -55,11 +63,11 @@ aoi <- list(
     type = "Polygon",
     coordinates = list(
       list(
-        c(33.78593974945852, 5.115816884114494),
-        c(33.78593974945852, -4.725410543134203),
-        c(41.94362577283266, -4.725410543134203),
-        c(41.94362577283266, 5.115816884114494),
-        c(33.78593974945852, 5.115816884114494)
+        c(minx, maxy),
+        c(minx, miny),
+        c(maxx, miny),
+        c(maxx, maxy),
+        c(minx, maxy)
       )
     )
   )
@@ -88,11 +96,14 @@ resp <- req |> req_perform()
 summary_data <- resp |> resp_body_string() |> fromJSON(flatten = TRUE)
 
 # Extract coordinates and convert to a spatial data frame (sf object)
-summary_data$x <- sapply(summary_data$geometry.coordinates, function(x) unlist(x)[1])
-summary_data$y <- sapply(summary_data$geometry.coordinates, function(x) unlist(x)[2])
+summary_data <- summary_data %>%
+  mutate(
+    x = sapply(geometry, function(g) fromJSON(g)$coordinates[1]),
+    y = sapply(geometry, function(g) fromJSON(g)$coordinates[2])
+  )
 
 # Convert to sf, drop extra geometry fields
-gdf <- st_as_sf(summary_data, coords = c("x", "y"), crs = 4326)[, c(1, 2, 5)]
+gdf <- st_as_sf(summary_data, coords = c("x", "y"), crs = 4326)
 ```
 
 ## Visualization
@@ -121,6 +132,5 @@ leaflet(gdf) %>%
   addLegend(
     pal = custom_pal, values = gdf$sum_pop_2020, title = "Population 2020 (Custom Binned Scale)",
     opacity = 1
-  ) %>%
-  setView(lng = 37.5, lat = 0, zoom = 6)  # Center the map based on AOI
+  )
 ```

docs/user-docs/space2stats_api_demo_R.md

@@ -0,0 +1,136 @@
+---
+title: "Space2Stats API Demo in R"
+output:
+  html_document:
+    df_print: paged
+---
+# R Example
+
+```R name="setup" tags=["remove_cell"]
+knitr::opts_chunk$set(echo = TRUE)
+library(httr2)
+library(jsonlite)
+library(sf)
+library(dplyr)
+library(leaflet)
+library(viridis)
+```
+
+## Set Up API Endpoints
+
+```R
+base_url <- "https://space2stats.ds.io"
+fields_endpoint <- paste0(base_url, "/fields")
+summary_endpoint <- paste0(base_url, "/summary")
+```
+
+## Fetch Available Fields
+
+```R
+# Set up the request to fetch available fields
+req <- request(base_url) |>
+  req_url_path_append("fields")  # Append the correct endpoint
+
+# Perform the request and get the response
+resp <- req |> req_perform()
+
+# Check the status code
+if (resp_status(resp) != 200) {
+    stop("Failed to get fields: ", resp_body_string(resp))
+}
+
+# Parse the response body as JSON
+available_fields <- resp |> resp_body_json()
+
+# Print the available fields in a simplified format
+print("Available Fields:")
+print(unlist(available_fields))
+```
+
+## Define Area of Interest (AOI)
+
+```R
+minx <- 29.038924
+miny <- -4.468958
+maxx <- 30.850461
+maxy <- -2.310523
+
+# Define Area of Interest (AOI) with NULL for properties to ensure it's treated as a valid dictionary
+aoi <- list(
+  type = "Feature",
+  properties = NULL,  # Empty properties
+  geometry = list(
+    type = "Polygon",
+    coordinates = list(
+      list(
+        c(minx, maxy),
+        c(minx, miny),
+        c(maxx, miny),
+        c(maxx, maxy),
+        c(minx, maxy)
+      )
+    )
+  )
+)
+```
+
+## Request Summary Data
+
+```R
+request_payload <- list(
+    aoi = aoi,
+    spatial_join_method = "centroid",
+    fields = list("sum_pop_2020"),
+    geometry = "point"
+)
+
+# Set up the base URL and create the request
+req <- request(base_url) |>
+  req_url_path_append("summary") |>
+  req_body_json(request_payload)
+
+# Perform the request and get the response
+resp <- req |> req_perform()
+
+# Turn response into a data frame
+summary_data <- resp |> resp_body_string() |> fromJSON(flatten = TRUE)
+
+# Extract coordinates and convert to a spatial data frame (sf object)
+summary_data <- summary_data %>%
+  mutate(
+    x = sapply(geometry, function(g) fromJSON(g)$coordinates[1]),
+    y = sapply(geometry, function(g) fromJSON(g)$coordinates[2])
+  )
+
+# Convert to sf, drop extra geometry fields
+gdf <- st_as_sf(summary_data, coords = c("x", "y"), crs = 4326)
+```
+
+## Visualization
+
+```R
+
+# Replace NA values in sum_pop_2020 with 0
+gdf$sum_pop_2020[is.na(gdf$sum_pop_2020)] <- 0
+
+# Create a custom binned color palette with non-uniform breaks
+# For example: 0 (distinct color), 1-200000 (gradient), 200001+ (another color)
+breaks <- c(0, 1, 1000, 10000, 50000, 100000, 200000, max(gdf$sum_pop_2020))
+
+custom_pal <- colorBin(palette = c("lightgray", "yellow", "orange", "red", "purple", "blue"),
+                       domain = gdf$sum_pop_2020, bins = breaks)
+
+# Create the leaflet map with custom binned coloring
+leaflet(gdf) %>%
+  addTiles() %>%  # Add default OpenStreetMap map tiles
+  addCircleMarkers(
+    radius = 3,  # Adjust size as needed
+    color = ~custom_pal(sum_pop_2020),
+    stroke = FALSE, fillOpacity = 0.7,
+    popup = ~paste("Hex ID:", hex_id, "<br>", "Population 2020:", sum_pop_2020)  # Add a popup with details
+  ) %>%
+  addLegend(
+    pal = custom_pal, values = gdf$sum_pop_2020, title = "Population 2020 (Custom Binned Scale)",
+    opacity = 1
+  )
+```