Exploring digitised maps in Trove¶

If you've ever poked around in Trove's 'map' zone, you might have noticed the beautiful deep-zoomable images available for many of the NLA's digitised maps. Even better, in many cases the high-resolution TIFF versions of the digitised maps are available for download.

I knew there were lots of great maps you could download from Trove, but how many? And how big were the files? I thought I'd try to quantify this a bit by harvesting and analysing the metadata.

The size of the downloadable files (both in bytes and pixels) are embedded within the landing pages for the digitised maps. So harvesting the metadata involves a number of steps:

  • Use the Trove API to search for maps that include the phrase "nla.obj" – this will filter the results to maps that have been digitised and are available through Trove
  • Work through the results, checking to see if the record includes a link to a digital copy.
  • If there is a digital copy, extract the embedded work data from the landing page.
  • Scrape the copyright status from the page.

2023 update! It turns out that embedded within the embedded data are MARC descriptions that include some other metadata that's not available through the API. This includes the map scale and coordinates. The coordinates can either be a point, or a bounding box. I've saved these values as well, and explored some ways of parsing and visualising the coordinates in this notebook.

The fields in the harvested dataset are:

  • title – title of the map
  • url – url to the map in the digitised file viewer
  • work_url – url to the work in the Trove map category
  • identifier – NLA identifier
  • date – date published or created
  • creators – creators of the map
  • publication – publication place, publisher, and publication date (if available)
  • extent – physical description of map
  • copyright_status – copyright status based on available metadata (scraped from web page)
  • scale – map scale
  • coordinates – map coordinates, either a point or a bounding box (format is 'W--E/N--S', eg: 'E 130⁰50'--E 131⁰00'/S 12⁰30'--S 12⁰40')
  • filesize_string – filesize string in MB
  • filesize – size of TIFF file in bytes
  • width – width of TIFF in pixels
  • height – height of TIFF in pixels
  • copy_role – I'm not sure what the values in this field signify, but as described below, you can use them to download high-res TIFF images

Getting map images¶

There are a couple of undocumented tricks that make it easy to programatically download images of the maps.

  • To view the JPG version, just add /image to the map url. For example: http://nla.gov.au/nla.obj-232162256/image
  • The JPG image will be at the highest available resolution, but you requests smaller versions using the wid parameter to specify a pixel width. For example: http://nla.gov.au/nla.obj-232162256/image?wid=400
  • There seems to be an upper limit for the resolution of the JPG versions, higher resolutions might be available via the TIFF file which you can download by adding the copy_role value to the url. For example, if the copy_role is 'm' this url will download the TIFF: http://nla.gov.au/nla.obj-232162256/m (note that some of these files are very, very large – you might want to check the filesize before downloading)

Setting things up¶

In [51]:
import datetime
import json
import os
import re
import time
import warnings
from functools import reduce

warnings.simplefilter(action="ignore", category=FutureWarning)

import altair as alt
import pandas as pd
import requests_cache
from bs4 import BeautifulSoup
from IPython.display import FileLink, display
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
from tqdm.auto import tqdm
from dotenv import load_dotenv

s = requests_cache.CachedSession()
retries = Retry(total=5, backoff_factor=1, status_forcelist=[502, 503, 504])
s.mount("https://", HTTPAdapter(max_retries=retries))
s.mount("http://", HTTPAdapter(max_retries=retries))

load_dotenv()
Out[51]:
True

You'll need a Trove API key to harvest the data.¶

In [2]:
# This creates a variable called 'api_key', paste your key between the quotes
API_KEY = "YOUR API KEY"

# Use an api key value from environment variables if it is available (useful for testing)
if os.getenv("TROVE_API_KEY"):
    API_KEY = os.getenv("TROVE_API_KEY")

Define some functions to do the work¶

In [44]:
def get_total_results(params):
    """
    Get the total number of results for a search.
    """
    these_params = params.copy()
    these_params["n"] = 0
    response = s.get("https://api.trove.nla.gov.au/v3/result", params=these_params, headers={"X-API-KEY": API_KEY})
    data = response.json()
    return int(data["category"][0]["records"]["total"])


def get_fulltext_url(links):
    """
    Loop through the identifiers to find a link to the digital version of the journal.
    """
    url = None
    for link in links:
        if link["linktype"] == "fulltext" and "nla.obj" in link["value"]:
            url = link["value"]
            break
    return url


def get_copyright_status(response=None, url=None):
    """
    Scrape copyright information from a digital work page.
    """
    if url and not response:
        response = s.get(url)
    if response:
        soup = BeautifulSoup(response.text, "lxml")
        try:
            copyright_status = str(
                soup.find("div", id="tab-access").find("p", class_="decorative").string
            )
            return copyright_status
        # No access tab
        except AttributeError:
            pass
    return ""


def get_work_data(url):
    """
    Extract work data in a JSON string from the work's HTML page.
    """
    response = s.get(url)
    try:
        work_data = json.loads(
            re.search(
                r"var work = JSON\.parse\(JSON\.stringify\((\{.*\})", response.text
            ).group(1)
        )
    except (AttributeError, TypeError):
        work_data = {}
    # else:
    # If there's no copyright info in the work data, then scrape it
    # if "copyrightPolicy" not in work_data:
    #    work_data["copyrightPolicy"] = get_copyright_status(response)
    if not response.from_cache:
        time.sleep(0.2)
    return work_data


def find_field_content(record, tag, subfield):
    """
    Loop through a MARC record looking for tag/subfield.
    If found, return the subfield value.
    """
    try:
        for field in record["datafield"]:
            if field["tag"] == tag:
                if isinstance(field["subfield"], list):
                    for sfield in field["subfield"]:
                        if sfield["code"] == subfield:
                            return sfield["content"]
                else:
                    if field["subfield"]["code"] == subfield:
                        return field["subfield"]["content"]
    except (KeyError, TypeError):
        pass
    return ""


def get_marc_field(work_data, tag, subfield):
    """
    Loop through all the MARC records in work metadata looking for a tag/subfield.
    If found, return the subfield value.
    """
    if "marcData" in work_data and work_data["marcData"]:
        for record in work_data["marcData"]["record"]:
            content = find_field_content(record, tag, subfield)
            if content:
                return content
    return ""


def format_bytes(size):
    """
    Format bytes as a human-readable string
    """
    # 2**10 = 1024
    if not size:
        return "", ""
    power = 2**10
    n = 0
    power_labels = {0: "", 1: "K", 2: "M", 3: "G", 4: "T"}
    while size > power:
        size /= power
        n += 1
    return size, power_labels[n] + "B"


def get_publication_details(work_data):
    """
    Get MARC values for publication details and combine into a single string.
    """
    parts = []
    for code in ["a", "b", "c"]:
        value = get_marc_field(work_data, 260, code)
        if value:
            parts.append(str(value))
    return " ".join(parts)


def get_map_data(work_data):
    """
    Look for file size information in the embedded data
    """
    map_data = {
        "filesize_string": "",
        "filesize": 0,
        "width": 0,
        "height": 0,
        "copy_role": ""
    }
    width = None
    height = None
    num_bytes = None
    try:
        # Make sure there's a downloadable version
        if (
            work_data.get("accessConditions") == "Unrestricted"
            and "copies" in work_data
        ):
            for copy in work_data["copies"]:
                width = ""
                height = ""
                num_bytes = ""
                copy_role = ""
                # Get the pixel dimensions
                if "technicalmetadata" in copy:
                    width = copy["technicalmetadata"].get("width", 0)
                    height = copy["technicalmetadata"].get("height", 0)
                # Get filesize in bytes
                elif (
                    copy["copyrole"] in ["m", "o", "i", "fd"]
                    and copy["access"] == "true"
                ):
                    num_bytes = copy.get("filesize", 0)
                    copy_role = copy.get("copyrole", "")
            size, unit = format_bytes(num_bytes)
            # Convert bytes to something human friendly
            if size:
                map_data["filesize_string"] = "{:.2f}{}".format(size, unit)
            map_data["filesize"] = num_bytes
            map_data["width"] = width
            map_data["height"] = height
            map_data["copy_role"] = copy_role

    except AttributeError:
        pass
    return map_data


def get_maps():
    """
    Harvest metadata about maps.
    """
    url = "http://api.trove.nla.gov.au/v3/result"
    maps = []
    params = {
        "q": '"nla.obj-"',
        "category": "image",
        "l-artType": "map",
        "l-availability": "y",
        "l-format": "Map/Single map",
        "bulkHarvest": "true",  # Needed to maintain a consistent order across requests
        "n": 100,
        "encoding": "json",
    }
    start = "*"
    total = get_total_results(params)
    with tqdm(total=total) as pbar:
        while start:
            params["s"] = start
            response = s.get(url, params=params, headers={"X-API-KEY": API_KEY})
            data = response.json()
            # If there's a startNext value then we get it to request the next page of results
            try:
                start = data["category"][0]["records"]["nextStart"]
            except KeyError:
                start = None
            for work in tqdm(
                data["category"][0]["records"]["work"], leave=False
            ):
                # Check to see if there's a link to a digital version
                try:
                    fulltext_url = get_fulltext_url(work["identifier"])
                except KeyError:
                    pass
                else:
                    if fulltext_url:
                        work_data = get_work_data(fulltext_url)
                        map_data = get_map_data(work_data)
                        obj_id = re.search(r"(nla\.obj\-\d+)", fulltext_url).group(1)
                        # Get basic metadata
                        # You could add more work data here
                        # Check the Trove API docs for work record structure
                        map_data["title"] = work.get("title", "")
                        map_data["url"] = fulltext_url
                        map_data["work_url"] = work.get("troveUrl", "")
                        map_data["identifier"] = obj_id
                        map_data["date"] = work.get("issued", "")
                        map_data["creators"] = "|".join(work.get("contributor", []))
                        map_data["publication"] = get_publication_details(work_data)
                        map_data["extent"] = work_data.get("extent", "")
                        # I think the copyright status scraped from the page (below) is more likely to be accurate
                        # map_data["copyright_policy"] = work_data.get("copyrightPolicy")
                        map_data["copyright_status"] = get_copyright_status(
                            url=fulltext_url
                        )
                        map_data["scale"] = get_marc_field(work_data, 255, "a")
                        map_data["coordinates"] = get_marc_field(work_data, 255, "c")
                        maps.append(map_data)
                        # print(map_data)
            pbar.update(100)
    return maps

Download map data¶

In [ ]:
maps = get_maps()

Convert to dataframe and save to CSV¶

In [47]:
# Convert to dataframe
# Convert dtypes converts numbers to integers rather than floats
df = pd.DataFrame(maps).convert_dtypes()
In [48]:
def merge_column(columns):
    values = []
    for value in columns:
        if isinstance(value, list):
            values += [str(v) for v in value if v]
        elif value:
            values.append(str(value))
    return " | ".join(sorted(set(values)))


def merge_records(df):
    # df["pages"].fillna(0, inplace=True)
    # df.fillna("", inplace=True)
    # df["pages"] = df["pages"].astype("Int64")

    # Add base dataset with columns that will always have only one value
    dfs = [df[["identifier", "url"]].drop_duplicates()]

    # Columns that potentially have multiple values which will be merged
    columns = [
        "title",
        "work_url",
        "date",
        "creators",
        "publication",
        "extent",
        "copyright_status",
        "scale",
        "coordinates",
        "filesize_string",
        "filesize",
        "width",
        "height",
        "copy_role"
    ]

    # Merge values from each column in turn, creating a new dataframe from each
    for column in columns:
        dfs.append(
            df.groupby(["identifier", "url"])[column].apply(merge_column).reset_index()
        )

    # Merge all the individual dataframes into one, linking on `text_file` value
    df_merged = reduce(
        lambda left, right: pd.merge(left, right, on=["identifier", "url"], how="left"), dfs
    )
    return df_merged
In [ ]:
df_merged = merge_records(df)
In [55]:
# Reorder columns
df_merged = df_merged[
    [
        "identifier",
        "title",
        "url",
        "work_url",
        "date",
        "creators",
        "publication",
        "extent",
        "copyright_status",
        "scale",
        "coordinates",
        "filesize_string",
        "filesize",
        "width",
        "height",
        "copy_role",
    ]
]

# Save to CSV
csv_file = f"single_maps_{datetime.datetime.now().strftime('%Y%m%d')}.csv"
df_merged.to_csv(csv_file, index=False)
display(FileLink(csv_file))
single_maps_20240608.csv

Let's explore the results¶

In [11]:
# Reload data from CSV if necessary
df = pd.read_csv(
    "https://raw.githubusercontent.com/GLAM-Workbench/trove-maps-data/main/single_maps.csv"
)

How many digitised maps are available?

In [14]:
print("{:,} maps".format(df.shape[0]))

35,042 maps
In [29]:
df.loc[df.duplicated(["url"], keep=False)].sort_values("url")
Out[29]:
identifier title url work_url date creators publication extent copyright_status scale coordinates filesize_string filesize width height copy_role mb
2314 nla.obj-1059119069 [Western Australia gold mining leases]. Depart... http://nla.gov.au/nla.obj-1059119069 https://trove.nla.gov.au/work/14409767 1905 Western Australia. Department of Mines [South Kensington : Science Museum Library, 19... 1 map ; 65 x 99 cm. Out of Copyright Scale [ca. 1:31 680] (E 122°10'30"/S 28°49'00") 1.19GB 1281565428 25062 17045 m 1222.196033
2315 nla.obj-1059119069 [Western Australia gold mining leases]. Depart... http://nla.gov.au/nla.obj-1059119069 https://trove.nla.gov.au/work/14409773 1905 Western Australia. Department of Mines [South Kensington : Science Museum Library, 19... 1 map ; 65 x 99 cm. Out of Copyright Scale [ca. 1:31 680] (E 122°10'30"/S 28°49'00") 1.19GB 1281565428 25062 17045 m 1222.196033
2471 nla.obj-1059122984 [Western Australia gold mining leases]. (1.2.0... http://nla.gov.au/nla.obj-1059122984 https://trove.nla.gov.au/work/14626171 1905 Western Australia. Department of Mines [London : Science Museum Library, 1905?] 1 map ; 65 x 98 cm. Edition Out of Copyright Scale [ca. 1:15 840] (E 121°09'/S 30°57') 1.14GB 1228526852 23731 17256 m 1171.614506
2467 nla.obj-1059122984 [Western Australia gold mining leases]. (1.2.0... http://nla.gov.au/nla.obj-1059122984 https://trove.nla.gov.au/work/14626082 1905 Western Australia. Department of Mines [London : Science Museum Library, 1905?] 1 map ; 65 x 98 cm. Edition Out of Copyright Scale [ca. 1:15 840] (E 121°09'/S 30°57') 1.14GB 1228526852 23731 17256 m 1171.614506
2472 nla.obj-1059123632 [Western Australia gold mining leases]. 20.5.0... http://nla.gov.au/nla.obj-1059123632 https://trove.nla.gov.au/work/14626181 1905 Western Australia. Department of Mines [London : Science Museum Library, 1905?] 1 map ; 65 x 98 cm. Edition Out of Copyright Scale [ca. 1:15 840] (E 121°09'/S 30°57') 1.20GB 1287582536 25084 17110 m 1227.934395
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4155 nla.obj-678124518 Geological atlas 1:50 000 series. Geological S... https://nla.gov.au/nla.obj-678124518 https://trove.nla.gov.au/work/16946369 1987 Geological Survey of Tasmania Hobart, Tas. : The Dept., 1987 1 map : col. ; 56 x 84 cm. on sheet 69 x 107 cm. In Copyright Scale 1:50 000 (E 148⁰00'--E 148⁰30'/S 41⁰15'--S 41⁰30') 1.16GB 1245749060 25436 16325 m 1188.038883
23268 nla.obj-893284845 Vegetation survey of Western Australia. mapped... https://nla.gov.au/nla.obj-893284845 https://trove.nla.gov.au/work/32952780 1973 Beard, J. S. (John Stanley), 1916-2011 Perth : Vegmap Publications, 1973 1 map ; 47 x 59 cm., on sheet 59 x 70 cm. + 1 ... In Copyright Scale 1:250,000 (E 123°00ʹ--E 124°30ʹ/S 33°00ʹ--S 34°00ʹ). 748.65MB 785014388 18087 14467 m 748.64806
7405 nla.obj-893284845 Vegetation survey of Western Australia. mapped... https://nla.gov.au/nla.obj-893284845 https://trove.nla.gov.au/work/19937156 1973 Beard, J. S. (John Stanley), 1916-2011 Perth : Vegmap Publications, 1973 1 map ; 47 x 59 cm., on sheet 59 x 70 cm. + 1 ... In Copyright Scale 1:250,000 (E 123°00ʹ--E 124°30ʹ/S 33°00ʹ--S 34°00ʹ). 748.65MB 785014388 18087 14467 m 748.64806
132 nla.obj-961623531 Australia 1:25 000 topographic survey. Produce... https://nla.gov.au/nla.obj-961623531 https://trove.nla.gov.au/work/10384783 1979 Western Australia. Department of Lands and Sur... Perth (W.A.) : Dept. of Lands and Surveys, 1979 1 map : col. ; 55 x 50 cm. In Copyright Scale 1:25,000 (E 115°45ʹ00ʺ--E 115°52ʹ30ʺ/S 32°15ʹ00ʺ--S 32°... 676.20MB 709045908 14078 16788 m 676.198872
3339 nla.obj-961623531 Australia 1:25 000 topographic survey.: Wellar... https://nla.gov.au/nla.obj-961623531 https://trove.nla.gov.au/work/159335939 1891-1979 Western Australia. Department of Lands and Sur... Perth (W.A.) : Dept. of Lands and Surveys, 1979 1 map : col. ; 55 x 50 cm. In Copyright Scale 1:25,000 (E 115°45ʹ00ʺ--E 115°52ʹ30ʺ/S 32°15ʹ00ʺ--S 32°... 676.20MB 709045908 14078 16788 m 676.198872

398 rows × 17 columns

How many of the maps have high-resolution downloads?

In [15]:
df.loc[df["filesize"].notnull()].shape
Out[15]:
(30738, 16)

What are the copy_role values?

In [16]:
df["copy_role"].value_counts()
Out[16]:
copy_role
m    30344
i      364
o       30
Name: count, dtype: Int64

How much map data is available for download?

In [17]:
size, unit = format_bytes(df["filesize"].sum())
print("{:.2f}{}".format(size, unit))

14.41TB

What's the copyright status of the maps?

In [18]:
df["copyright_status"].value_counts()
Out[18]:
copyright_status
Out of Copyright            25281
In Copyright                 8618
Edition Out of Copyright      631
Copyright Undetermined        349
Copyright Uncertain           110
Unknown                        22
Edition In Copyright            4
Name: count, dtype: Int64

Let's show the copyright status as a chart...

In [19]:
counts = df["copyright_status"].value_counts().to_frame().reset_index()
counts.columns = ["status", "count"]
alt.Chart(counts).mark_bar().encode(
    y="status:N", x="count", tooltip="count"
).properties(height=200)
Out[19]:

Let's look at the sizes of the download files. To make this easier we'll divide the filesizes into ranges (bins) and count the number of files in each range.

In [20]:
# Convert bytes to mb
df["mb"] = df["filesize"] / 2**10 / 2**10
# Create 500mb-sized bins and count the number of files in each bin
sizes = (
    pd.cut(df["mb"], bins=[0, 500, 1000, 1500, 2000, 3000, 3500])
    .value_counts()
    .to_frame()
    .reset_index()
)
sizes.columns = ["mb", "count"]
# Convert intervals to strings for display in chart
sizes["mb"] = sizes["mb"].astype(str)
sizes
Out[20]:
mb count
0 (0, 500] 16143
1 (500, 1000] 11454
2 (1000, 1500] 2733
3 (1500, 2000] 311
4 (2000, 3000] 84
5 (3000, 3500] 12
In [21]:
alt.Chart(sizes).mark_bar().encode(
    x=alt.X("mb:N", sort=None), y="count:Q", tooltip="count:Q"
).properties(width=400)
Out[21]:

So while most are less than 500MB, more than 10,000 are between 0.5 and 1GB!

What's the biggest file available for download?

In [22]:
df.iloc[df["filesize"].idxmax()]
Out[22]:
identifier                                         nla.obj-2458846831
title               Geologic map of the Arabian Peninsula / compil...
url                             https://nla.gov.au/nla.obj-2458846831
work_url                       https://trove.nla.gov.au/work/12332257
date                                                             1963
creators                                     Geological Survey (U.S.)
publication                       Washington, D.C. : The Survey, 1963
extent                                   1 map : col. ; 113 x 132 cm.
copyright_status                                         In Copyright
scale                                             Scale 1:2,000,000 ;
coordinates                               (E 34°--E 61°/N 32°--N 12°)
filesize_string                                                3.64GB
filesize                                                   3907679404
width                                                           43211
height                                                          30144
copy_role                                                           m
mb                                                        3726.653484
Name: 1536, dtype: object

All downloads greater than 3GB.

In [23]:
df.loc[(df["filesize"] / 2**10 / 2**10 / 2**10) > 3]
Out[23]:
identifier title url work_url date creators publication extent copyright_status scale coordinates filesize_string filesize width height copy_role mb
1536 nla.obj-2458846831 Geologic map of the Arabian Peninsula / compil... https://nla.gov.au/nla.obj-2458846831 https://trove.nla.gov.au/work/12332257 1963 Geological Survey (U.S.) Washington, D.C. : The Survey, 1963 1 map : col. ; 113 x 132 cm. In Copyright Scale 1:2,000,000 ; (E 34°--E 61°/N 32°--N 12°) 3.64GB 3907679404 43211 30144 m 3726.653484
2611 nla.obj-2567709383 Map of the coastal plain of British Guiana https://nla.gov.au/nla.obj-2567709383 https://trove.nla.gov.au/work/152215030 1955 Bleackley, D. (David) [S.l.] : Geological Survey of British Guiana, ... 1 map : col. ; 88 x 205 cm. In Copyright Scale [ca. 1:143,000]. (W 60°00ʹ--W 57°00ʹ/N 9°00ʹ--N 6°00ʹ). 3.08GB 3305391052 49731 22155 m 3152.266552
5240 nla.obj-591001246 Map of the City of Rangoon and suburbs 1928-29... http://nla.gov.au/nla.obj-591001246 https://trove.nla.gov.au/work/182743876 1932 Geological Survey of India 1 map on 4 sheets : colour ; 154 x 126 cm, sheets Out of Copyright Scale 1:12,000. 1 in. = 1000 ft. (E 96°06ʹ--E 96°13ʹ/N 16°53ʹ--N 16°44ʹ). 3.38GB 3623879488 31769 38023 m 3456.000793
6241 nla.obj-3009772762 Shqipëria, hartë fiziko-politike : shkalla 1... https://nla.gov.au/nla.obj-3009772762 https://trove.nla.gov.au/work/191812727 1965 Samimi, Ergjin 1 map on 3 sheets : color ; 173 x 91 cm, sheet... In Copyright Scale 1:200,000. 1 cm to 2 km ; (E 18°58ʹ--E 21°12ʹ/N 42°40ʹ--N 39°35ʹ). 3.04GB 3266078212 23106 47117 m 3114.774906
7871 nla.obj-568387103 Peta geologi teknik daerah Jakarta - Bogor = E... http://nla.gov.au/nla.obj-568387103 https://trove.nla.gov.au/work/20208553 1970 Indonesia. Direktorat Geologi 1 map : colour ; 157 x 107 cm In Copyright Scale 1:50,000 (E 106°33'00"--E 106°59'00"/S 5°59'00"--S 6°38... 3.05GB 3279210576 26384 41429 m 3127.298904
8362 nla.obj-400826638 Nyūginia-tō zenzu / Taiwan Sōtokufu Gaijibu... http://nla.gov.au/nla.obj-400826638 https://trove.nla.gov.au/work/205481810 1942 Taiwan 1 map on 4 sheets : colour ; 172 x 99 cm Out of Copyright Scale 1:5,000,000 ; (E 126°00ʹ--E 156°00ʹ/N 4°00ʹ--S 12°00ʹ). 3.04GB 3264456500 42659 25508 m 3113.228321
11917 nla.obj-568387099 Geological map of Djawa and Madura / compiled ... http://nla.gov.au/nla.obj-568387099 https://trove.nla.gov.au/work/218208895 1963 Indonesia. Direktorat Geologi 1 map : colour ; 78 x 216 cm. In Copyright Scale 1:500,000 (E 104°58ʹ28ʺ--E 113°98ʹ28ʺ/S 5°30ʹ00ʺ--S 9°00... 3.08GB 3311801600 52593 20990 m 3158.380127
14886 nla.obj-1954049619 A new chart of the South Pacific Ocean, includ... https://nla.gov.au/nla.obj-1954049619 https://trove.nla.gov.au/work/237421392 1849-1857 James Imray and Son 1 map ; 96.4 x 183.0 cm Edition Out of Copyright Scale approximately 1:11,000,000 at the equator (E 111°--W 60°/N 20°--S 60°). 3.00GB 3223026784 44606 24085 m 3073.717865
15311 nla.obj-2618718155 Proposed plan for the site for the federal cap... https://nla.gov.au/nla.obj-2618718155 https://trove.nla.gov.au/work/239126400 1911 Wilson, George, died 1923 1 map : colour ; 141 x 141 cm Out of Copyright Scale 1:4,800 ; (E 149°08'/S 35°18'). 3.12GB 3344969196 33600 33184 m 3190.011211
15477 nla.obj-2824965115 Map of the mandated territory of New Guinea / ... https://nla.gov.au/nla.obj-2824965115 https://trove.nla.gov.au/work/239997009 1925 Krahe, R. E. 1 map : transparent architectural linen ; 210 ... In Copyright Scale 1:1,000,000 (E 140°50'00"--E 159°41'00"/S 0°33'00"--S 11°5... 3.37GB 3622362060 53028 22770 m 3454.553661
34455 nla.obj-230705067 Plan shewing pastoral leases and claims in the... https://nla.gov.au/nla.obj-230705067 https://trove.nla.gov.au/work/8818311 1885-1950 South Australia. Surveyor-General's Office Adelaide : Surveyor General's Office, 1885 1 map on 3 sheets ; 169.1 x 99.7 cm., on sheet... Out of Copyright Scale [1:1 000 000]. 16 miles to 1 inch. <NA> 3.08GB 3308608288 25576 43121 m 3155.334747

The widest image?

In [24]:
df.iloc[df["width"].idxmax()]
Out[24]:
identifier                                          nla.obj-636346192
title               Land status petroleum mining agreement in resp...
url                               http://nla.gov.au/nla.obj-636346192
work_url                      https://trove.nla.gov.au/work/230363372
date                                                             1968
creators                               Brunei Shell Petroleum Company
publication                                                          
extent                                            1 map ; 286 x 58 cm
copyright_status                                         In Copyright
scale                                                  Scale 1:10,000
coordinates         (E 114°09ʹ53ʺ--E 114°23ʹ34ʺ/N 4°38ʹ42ʺ--N 4°32...
filesize_string                                                2.80GB
filesize                                                   3008938460
width                                                           68453
height                                                          14652
copy_role                                                           m
mb                                                        2869.547329
Name: 13749, dtype: object

The tallest image?

In [25]:
df.iloc[df["height"].idxmax()]
Out[25]:
identifier                                         nla.obj-2824964225
title               Traverse of the Ramu River, navigated by the "...
url                             https://nla.gov.au/nla.obj-2824964225
work_url                       https://trove.nla.gov.au/work/36757550
date                                                        1921-1945
creators                   Stanley, Evan R. (Evan Richard), 1885-1924
publication                                                          
extent                   1 map : on architectural linen ; 410 x 76 cm
copyright_status                                         In Copyright
scale                                                  Scale 1:31,760
coordinates                  (E 144°35'--E 144°50'/S 4°01'--S 5°11').
filesize_string                                                2.85GB
filesize                                                   3057135688
width                                                           13840
height                                                          73630
copy_role                                                           m
mb                                                        2915.511787
Name: 31282, dtype: object

Created by Tim Sherratt for the GLAM Workbench.

Work on this notebook was originally supported by the Humanities, Arts and Social Sciences (HASS) Data Enhanced Virtual Lab.