# Spatial Epidemiology with mongolstats

    library(mongolstats)
    library(sf)
    library(dplyr)
    library(ggplot2)
    nso_options(mongolstats.lang = "en")

    # Global theme with proper margins to prevent text cutoff
    theme_set(
      theme_minimal(base_size = 11) +
        theme(
          plot.margin = margin(10, 10, 10, 10),
          plot.title = element_text(size = 13, face = "bold"),
          plot.subtitle = element_text(size = 10, color = "grey40"),
          legend.text = element_text(size = 9),
          legend.title = element_text(size = 10)
        )
    )

## Overview

Geographic analysis is essential for understanding health disparities and targeting interventions. This guide demonstrates spatial epidemiology using Mongolia’s aimag-level (provincial) health data.

## Getting Boundary Data

Mongolia’s administrative boundaries are available at three levels:

    # ADM0: National boundary
    country <- mn_boundaries(level = "ADM0")

    # ADM1: Aimags (21 provinces + Ulaanbaatar)
    aimags <- mn_boundaries(level = "ADM1")

    # ADM2: Soums (districts)
    soums <- mn_boundaries(level = "ADM2")

    # Quick preview
    aimags |>
      ggplot() +
      geom_sf(fill = "white", color = "grey30", size = 0.3) +
      theme_void() +
      labs(title = "Mongolia's 21 Aimags + Ulaanbaatar")

## Case Study: Maternal Mortality Geography

### Understanding Regional Disparities

Maternal mortality is a critical indicator of health system performance and equity.

    # Fetch maternal mortality data for all aimags (2020-2024)
    # We'll calculate a 5-year average to smooth out year-to-year variability
    # This is important because small populations can have unstable rates

    # The table is monthly; map month labels ("YYYY-MM") to codes for 2020-2024
    mmr_tbl <- "DT_NSO_2100_050V1" # MMR per 100,000 live births
    mmr_months <- nso_dim_values(mmr_tbl, "Month", labels = "en") |>
      filter(label_en >= "2020-01", label_en <= "2024-12") |>
      pull(code)

    mmr_data <- nso_data(
      tbl_id = mmr_tbl,
      selections = list(
        "Region" = nso_dim_values(mmr_tbl, "Region")$code,
        "Month" = mmr_months
      ),
      labels = "en"
    ) |>
      filter(!Region %in% c("0", "1", "2", "3", "4", "511")) |> # Exclude Total, Regions, and duplicate UB
      mutate(
        Region_en = trimws(Region_en),
        # Standardize region names to match the geographic boundaries
        Region_en = dplyr::case_match(
          Region_en,
          "Bayan-Ulgii" ~ "Bayan-Ölgii",
          "Uvurkhangai" ~ "Övörkhangai",
          "Khuvsgul" ~ "Hovsgel",
          "Umnugovi" ~ "Ömnögovi",
          "Tuv" ~ "Töv",
          "Sukhbaatar" ~ "Sükhbaatar",
          .default = Region_en
        )
      ) |>
      # Calculate 5-year average to reduce random variation
      group_by(Region_en) |>
      summarise(value = mean(value, na.rm = TRUE), .groups = "drop")

    # Preview data
    mmr_data |>
      arrange(desc(value)) |>
      select(Region_en, value) |>
      head(10)

### Creating a Choropleth Map

    # Join health data to geographic boundaries for spatial analysis
    mmr_map <- aimags |>
      left_join(mmr_data, by = c("shapeName" = "Region_en"))

    # Create choropleth map
    p <- mmr_map |>
      ggplot() +
      geom_sf(aes(fill = value), color = "white", size = 0.2) +
      scale_fill_viridis_c(
        option = "rocket",
        direction = -1,  # dark = high mortality (concerning)
        name = "MMR\n(per 100k)",
        labels = scales::label_number()
      ) +
      labs(
        title = "5-Year Average Maternal Mortality Ratio (2020-2024)",
        subtitle = "Deaths per 100,000 live births (Mean)",
        caption = "Source: NSO Mongolia"
      ) +
      theme_void() +  # removes axes for clean map appearance
      theme(
        plot.title = element_text(face = "bold", size = 16),
        plot.subtitle = element_text(color = "grey40"),
        legend.position = "bottom",          # bottom legend maximizes map width
        legend.title = element_text(size = 10, face = "bold"),
        legend.key.width = unit(1.5, "cm")   # wider legend key for continuous scale
      )

    p  # print static ggplot

## Case Study: Infant Mortality Hot Spots

### Identifying High-Risk Regions

    # Get infant mortality rates
    imr_tbl <- "DT_NSO_2100_015V1" # IMR per 1,000 live births (Monthly)

    # Get metadata
    months <- nso_dim_values(imr_tbl, "Month", labels = "en")
    months_2024 <- months |>
      filter(grepl("2024", label_en)) |>
      pull(code)

    imr_data <- nso_data(
      tbl_id = imr_tbl,
      selections = list(
        "Region" = nso_dim_values(imr_tbl, "Region")$code,
        "Month" = months_2024
      ),
      labels = "en"
    ) |>
      filter(nchar(Region) == 3) |> # Keep only Aimags and Ulaanbaatar
      mutate(
        Region_en = trimws(Region_en),
        Region_en = dplyr::case_match(
          Region_en,
          "Bayan-Ulgii" ~ "Bayan-Ölgii",
          "Uvurkhangai" ~ "Övörkhangai",
          "Khuvsgul" ~ "Hovsgel",
          "Umnugovi" ~ "Ömnögovi",
          "Tuv" ~ "Töv",
          "Sukhbaatar" ~ "Sükhbaatar",
          .default = Region_en
        )
      ) |>
      # Calculate annual average
      group_by(Region_en) |>
      summarise(value = mean(value, na.rm = TRUE), .groups = "drop") |>
      mutate(
        # Classify risk levels
        risk_category = case_when(
          value < 10 ~ "Low (<10)",
          value < 20 ~ "Medium (10-20)",
          value < 30 ~ "High (20-30)",
          TRUE ~ "Very High (≥30)"
        ),
        risk_category = factor(
          risk_category,
          levels = c("Low (<10)", "Medium (10-20)", "High (20-30)", "Very High (≥30)")
        )
      )

    # Create risk category map
    p <- aimags |>
      left_join(imr_data, by = c("shapeName" = "Region_en")) |>
      ggplot() +
      geom_sf(aes(fill = risk_category), color = "white", size = 0.2) +
      scale_fill_manual(
        values = c(
          "Low (<10)" = "#27ae60",       # green = good outcome
          "Medium (10-20)" = "#f1c40f",  # yellow = caution
          "High (20-30)" = "#e67e22",    # orange = concerning
          "Very High (≥30)" = "#c0392b"  # red = critical
        ),
        na.value = "grey90",  # missing data shown in light grey
        name = "Risk Level\n(IMR)",
        drop = FALSE  # show all levels even if not present in data
      ) +
      labs(
        title = "Infant Mortality Risk Categories (2024 Average)",
        subtitle = "Deaths per 1,000 live births",
        caption = "Source: NSO Mongolia"
      ) +
      theme_void() +
      theme(
        plot.title = element_text(face = "bold", size = 16),
        plot.subtitle = element_text(color = "grey40"),
        legend.position = "bottom",          # bottom legend maximizes map width
        legend.title = element_text(size = 10, face = "bold")
      )

    p  # print static ggplot

## Tips for Spatial Epidemiology

1. Check data completeness: Not all aimags may have data for all indicators
2. Use appropriate scales: Choose color scales that highlight health disparities
3. Add context: Include reference lines (e.g., national average) when relevant
4. Consider population size: Normalize rates by population when comparing regions
5. Temporal analysis: Create animated maps to show geographic trends over time

## Next Steps

- Discover Health Data: Return to the Discovery Guide
- Learn More: Explore all functions in the Reference