Creating a Current Temperature Map

.title[
# Creating a Current Temperature Map
]
.author[
### Luke Tierney
]
.institute[
### University of Iowa
]
.date[
### 2026-05-08
]

---

## Finding the Current Temperature

There are several options for obtaining current weather information,
including

- _Open Weather Map_
- The _National Weather Service_

---

[_Open Weather Map_](https://openweathermap.org/) provides an
[API](https://openweathermap.org/api) for returning weather information
in JSON or XML format.

A query requesting the current temperature in Iowa City in format XML
format would use a URL of the form

<https://api.openweathermap.org/data/2.5/weather?q=Iowa+City,IA&mode=xml&appid=>_key_

<https://api.openweathermap.org/data/2.5/weather?lat=41.66&lon=-91.53&mode=xml&appid=>_key_

with _key_ replaced by your API key (free, but requires registration).

---

Here is a simple function to obtain the current temperature for from _Open
Weather Map_ based on latitude and longitude:

``` r
library(xml2)
findTempOWM <- function(lat, lon) {
    base <- "https://api.openweathermap.org/data/2.5/weather"
    url <- sprintf("%s?lat=%f&lon=%f&mode=xml&units=Imperial&appid=%s",
                   base, lat, lon, OWMkey)
    page <- read_xml(url)
    as.numeric(xml_text(xml_find_first(page, "//temperature/@value")))
}
```

For Iowa City you would use

``` r
findTempOWM(41.7, -91.5)
## [1] 50.4
```

This function should be robust since the format of the response is
documented and should not change.

Using commercial web services should be done with care as there are
typically limitations and license terms to be considered.

They may also come and go: Google’s API was shut down in 2012.

---

The _National Weather Service_ provides a site that produces forecasts
in a web page for a URL like this:

<https://forecast.weather.gov/zipcity.php?inputstring=Iowa+City,IA>

This function uses the _National Weather Service_ site to find the
current temperature by parsing the HTML data in the response:

``` r
findTempGov <- function(citystate) {
    url <- paste("https://forecast.weather.gov/zipcity.php?inputstring",
                 url_escape(citystate),
                 sep = "=")
    page <- read_html(url)
    xpath <- "//p[@class=\"myforecast-current-lrg\"]"
    tempNode <- xml_find_first(page, xpath)
    nodeText <- xml_text(tempNode)
    as.numeric(sub("([-+]?[[:digit:]]+).*", "\\1", nodeText))
}
```

For Iowa City you can use

``` r
findTempGov("Iowa City, IA")
## [1] 47
```
--

This will need to be revised whenever the format of the page changes,
as happened some years ago .

---

Murrell’s Data Technologies book discusses XML, XPATH queries,
regular expressions, and how to work with these in R.

Some other resources for regular expressions:

* [Regular-Expressions.info](https://www.regular-expressions.info/)

* [_R for Data Science_](https://r4ds.hadley.nz/)

Sometimes the server may either be overloaded or be rejecting requests
from the same address that are too close together, so it may not be
useful for collecting temperatures at multile locations.

---

The _National Weather Service_ also provides several APIs, including a
REST API that returns JSON (XML may be available but didn't seem to
work right for me) that use a url like this:
  
  <https://forecast.weather.gov/MapClick.php?lat=41.7&lon=-91.5&FcstType=json>

You can read the temperature from this API using the `fromJSON()`
function from the `jsonlite` package:

``` r
findTempGovJSON <- function(lat, lon) {
    fmt <-
        "http://forecast.weather.gov/MapClick.php?lat=%f&lon=%f&FcstType=json"
    icjson <- jsonlite::fromJSON(sprintf(fmt, lat, lon))
    as.numeric(icjson$currentobservation$Temp)
}
findTempGovJSON(41.7, -91.5)
## [1] 47
```

---

A small selection of Iowa cities:

``` r
places <- c("Ames", "Burlington", "Cedar Rapids", "Clinton",
            "Council Bluffs", "Des Moines", "Dubuque", "Fort Dodge",
            "Iowa City", "Keokuk", "Marshalltown", "Mason City",
            "Newton", "Ottumwa", "Sioux City", "Waterloo")
```

Currently the OWM API seems most reliable.

To look up temperatures with that API, and to plot them on a map, we
need to find the locations of these cities.

---

We can obtain a file of geocoded cities and read it into R:

``` r
if (! file.exists("cities.csv")) {
    download.file("http://www.stat.uiowa.edu/~luke/data/cities.csv.zip",
                  "cities.csv.zip")
    unzip("cities.csv.zip")
}

cities <- read.csv("cities.csv", header = FALSE)
names(cities) <- c("City", "State", "Lat", "Lon")
head(cities)
##           City State      Lat        Lon
## 1      ROBERTS    ID 43.69892 -112.17320
## 2  HODGENVILLE    KY 37.55932  -85.70727
## 3 WILLIAMSPORT    TN 35.72263  -87.21270
## 4       MAUMEE    OH 41.57125  -83.68504
## 5        BERNE    NY 42.60224  -74.15462
## 6     MONCLOVA    OH 41.57725  -83.77238
```

---

Select the cities we want and find their temperatures:

``` r
library(dplyr)
```

``` r
temploc <- filter(cities, City %in% toupper(places), State == "IA") |>
    mutate(temp = mapply(findTempOWM, Lat, Lon))
head(temploc)
##           City State      Lat       Lon  temp
## 1   FORT DODGE    IA 42.43860 -94.16659 39.22
## 2    IOWA CITY    IA 41.65789 -91.53309 49.05
## 3 MARSHALLTOWN    IA 42.03610 -92.99887 49.14
## 4 CEDAR RAPIDS    IA 41.97666 -91.67315 49.59
## 5   BURLINGTON    IA 40.89841 -91.16439 50.88
## 6   MASON CITY    IA 43.15272 -93.19944 48.88
```

Remove any rows with missing temperatures:

``` r
temploc <- filter(temploc, ! is.na(temp))
```

---

We can use the `annotate_borders()` function from `ggplot2` along with
`geom_text()` to show the results:

.pull-left.small-code[

``` r
library(ggplot2)
library(ggthemes)
tpoints <- geom_text(aes(x = Lon, y = Lat,
                         label = round(temp)),
                     color = "red",
                     fontface = "bold",
                     data = temploc)

p <- ggplot() +
    annotation_borders("county", "iowa") +
    tpoints +
    coord_map() +
    theme_map()

p
```
]
.pull-right[
![](weather_files/figure-html/basic-map-1.png)
]

---

To add contours we can use `interp()` from the `interp` package and the
`geom_contour()` function:

.pull-left.small-code[

``` r
library(interp)
surface <- with(temploc,
                interp(Lon, Lat, temp,
                       linear = FALSE))

srfc <- expand.grid(Lon = surface$x, Lat = surface$y)
srfc$temp <- as.vector(surface$z)
tconts <-
    geom_contour(aes(x = Lon, y = Lat, z = temp),
                 data = srfc,
                 na.rm = TRUE,
                 bins = 6)

p + tconts + tpoints
```
]
.pull-right[
![](weather_files/figure-html/contour-map-1.png)
]

---
    
The `ggmap` package provides further map support, including background
maps from [Google](http://maps.google.com),
[Stamen](https://maps.stamen.com/),
[OpenStreatMap](https://www.openstreetmap.org/), and
[Stadia](https://stadiamaps.com/).

``` r
library(ggmap)
```

Stamen maps are hosted at Stadia Maps. An API key is required, but no
credit card is necessary to sign up and there is a free tier for
non-commercial use.

``` r
register_stadiamaps(StadiaKey, write = FALSE)
maptype <- "stamen_terrain"
map <- get_stadiamap(c(-97.2, 40.4, -89.9, 43.6), zoom = 8, maptype = maptype)
```

---

`ggmap()` creates a `ggplot` object, sets the coordinate system, and
makes some theme adjustments:

``` r
ggmap(map) + theme_map()
```
]
.pull-right[
![](weather_files/figure-html/terrain-1.png)
]

---

Add the county borders, contours, and temperatures.

``` r
ggmap(map) +
    annotation_borders("county", "iowa") +
    tconts +
    tpoints +
    theme_map()
```
]
.pull-right[
![](weather_files/figure-html/contour-terrain-1.png)
]

//adapted from Emi Tanaka's gist at //https://gist.github.com/emitanaka/eaa258bb8471c041797ff377704c8505