Introducing Nearmap for Post-catastrophe Response
Post Catastrophe Imagery and AI-derived property damage and condition data unite to help insurers process customer claims more efficiently.
Post Catastrophe Imagery and AI-derived property damage and condition data unite to help insurers process customer claims more efficiently.
Source: Ch 9 News Adelaide, 7 March 2022
After our ground-breaking national study of tree cover in Australian capital cities, Nearmap recently followed up with a new first. Instead of going wide across our Australian coverage, we went deep in one capital city: Adelaide. How green is your city?
The story has been featured by several media outlets, including online articles in the Adelaide Advertiser, Australian Property Journal, and ABC News.
Covering the 10-year span from 2011-2021, we analysed the changes in residential tree canopy across 281 suburbs in Adelaide.* Rather than a comparison across only two dates, which risks artefacts in one date skewing the result, we used seasonally-matched data from nine distinct points in time across the decade to confirm any trends.
We’ve received some questions along the way, including how we arrived at the numbers. This blog — part one in a series of four — provides the technical background, and answers some of the key questions. A link to part 2 of the series is provided at the end of this article.
The technical back-story for the previous national study is also worth a read as it gives good background for the Nearmap artificial intelligence capability, and the fundamentals of the analysis.
I'll assume you've read the articles linked above to avoid too much repetition! They cover concepts like residential mesh blocks, deep learning and machine learning, and the Nearmap rich historical archive of imagery that organisations can use to derive powerful insights.
The fundamental aspects of the national study were continued for consistency. Key points include:
In order to acquire robust statistics for the 10-year change, we ran our latest Nearmap AI system on imagery in the January-March window for every second year between 2011 and 2015, and then every year after that until 2021. While this is a small subset of the total surveys (captured around six times per year since 2009), viewing the same window each year allowed us to ignore the small seasonal effects introduced by leaf-off deciduous trees having slightly smaller footprints. How green is your city?
The reason to cover nine different years in the study, and only report on the first and last (2011 and 2021) was to focus on the net change over the decade. The trend between the two dates supports the overall conclusion with numerous analysis dates. This removes the chance of a single unusual year producing a biased result (e.g. drought/bushfire, or less-favourable capture conditions). It also serves to help when we deep-dive on individual suburbs, to tell the story of the exact nature and timing of changes that occurred.
A series of statistical analyses were performed on the 281 suburbs that were fully covered both in the 2011 and 2021 AI data. Statistics within a suburb were calculated on a per unit area basis. Specifically, residential tree or building cover first calculated the percentage by area of tree canopy or building in each residential mesh block in the suburb. The average of all the residential mesh blocks in the suburb was then taken. While this is not strictly the area coverage within the suburb, it reduces the impact of occasional unsuitably large mesh blocks on the average (which tend to occur in more volatile areas of change such as a farm that has been recently turned into a new housing development), and provides a better representation of the lived experience of typical residents within that suburb.
Adelaide-wide statistics are provided on the suburbs as a group. In general, the statistics and graphs describe the count of the suburbs (e.g. 18 suburbs with >10% gain in residential tree cover). For these, the area of the suburb is not taken into account. This means that a larger, less densely populated suburb carries the same weight as a smaller urban one, again to focus better on the lived experience of residents.
Where multiple surveys were available in the January–March window of a given year, the average result was taken at a mesh-block level of surveys within that window. This was reasonably common, due to survey overlaps, and the six times per year captures (granting a January and March survey on a given location, for example). This adds an additional level of robustness to the single date measures.
Where 10-year comparisons are made, we compare this averaged result from 2011 and 2021. Any suburb without full coverage on both dates was excluded.
Where graphs over time are shown, a small amount of smoothing is applied, to ensure that small amounts of noise are not interpreted as genuine changes. A rolling mean with Gaussian window, standard deviation 1 year was applied.
When doing a new study, getting the numbers right is critical to our mission, and we spend a huge amount of time ensuring this is the case.
Firstly, the Nearmap AI documentation pages describe in a fair degree of detail how the data is produced, verified and versioned. We used exclusively Gen 4 Nearmap AI content for this study, with the Building Footprint and Vegetation AI packs.
Secondly, large amounts of qualitative analysis were performed, both exploring the behaviour on individual suburbs, and visually comparing against the only relevant source of reference, a LiDAR map produced by blending two years’ worth of LiDAR tree canopy measurements across Adelaide. We’ll provide more detail on the comparison to LiDAR in a follow-up post.
With these results, we’ll commence with the ‘headline’ summaries, working backwards to the individual suburbs and raw data comparisons.
This boxplot shows the distribution of the 281 suburbs, and the relative change from the 2011 to 2021 levels. For example, a suburb dropping from 10% residential tree cover to 8% residential tree cover would register as a 20% relative change.
This figure gives us the median – where half the suburbs lost at least 9.8% of their tree cover over the decade.
The horizontal bar chart above shows the same information in a different way. Suburbs were categorised into relative change brackets of (< -10%), (-10% to -1%), (-1% to 1%), (1% to 10%), (> 10%).
In the chart, it is possible to see 131 suburbs (or 47% of them) experienced a relative loss exceeding 10% (dark pink). A smaller portion of 18 suburbs (6%) saw a relative gain of more than 10%.
In the map below, we re-use the same colour scheme from the horizontal bar graph, and plot the suburbs spatially (with the ABS 2021 "SAL" suburb outlines). The map shows some clearly clustered areas of growth/loss, but also some interesting locations in the north, where high growth and high loss are interspersed.
Adelaide suburbs showing ABS 2021 “SAL” suburb outlines
The following map is a sub section of the study area, showing only the residential mesh blocks that were used to form the suburb-level analysis.
Residential mesh blocks for suburb-level analysis
Since revealing the statistics, we have had some questions around ignoring the non-residential mesh blocks (such as parks and industrial areas). These numbers demonstrate results with and without those mesh blocks, and show a fairly similar drop over the decade. This indicates that the changes are sufficiently large and widespread that the precise analytical approach does not have an outsized impact on the study.
For these numbers, we ignore suburb boundaries, and take all mesh blocks in the study area:
While the overall story saw tree loss outweigh growth, many things can be said about analysis and the benefit of hindsight.
In this post, we explored high level, aggregated statistics about city-wide changes. The focus of the analysis was the relative change from 2011 residential tree cover to 2021. However, we see a similar story play out when the non-residential areas were included.
While the focus here was on Adelaide, the Nearmap capture program, visual and artificial intelligence products mean that this study is possible to repeat in any of the hundreds of urban areas in our capture program in four countries, and valid comparisons may be drawn due to the consistently applied methodology.
We'll be curious to see what comes of this work, and are eager to collaborate with organisations that want to both understand where tree canopy (and our dozens of other AI-derived layers) in their area of interest has been in the past, and want to work together to actively monitor how they are shaped in future.
In the next post — part two of this four-part series — we’ll look at the temporal change in residential building and tree cover throughout the decade for a number of suburbs, validating the data at a deeper level, and uncovering some fascinating stories of change.
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Some LGAs list the same suburb as both the 'greenest suburb' and 'least green suburb'. This means that there was only a single residential suburb within the LGA fully covered by the analysis.
Nearmap does not warrant or accept any liability in relation to the accuracy, correctness or reliability of the data provided as part of the Nearmap Leafiest Suburbs analysis. The Nearmap Leafiest Suburbs analysis is based on Nearmap AI data, which detects trees approximately 2m or higher. The national aerial data was collected Oct 2020-March 2021. Results were aggregated at mesh block level using the 2021 Australian Bureau of Statistics definitions. Approximately 5,000 suburbs were included in the analysis, where Nearmap AI coverage exceeded 99%. The top suburbs are those with the greatest percent tree cover in each 2021 SA4 region, and where there is a minimum population of 1,000 residents (2016 census). For suburbs that span multiple LGAs, that suburb is assigned to the LGA that contains the highest proportion of that suburb’s area. City-based metrics analyse all Nearmap AI covered suburbs within the relevant ABS GCCSA region. For the capital city suburb breakdowns, we also refined the analysis to only include ‘residential’ mesh blocks. All percentage figures have been rounded to the closest whole number.
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About the author:
With degrees in electrical engineering and physics — and a passion for machine learning — Dr Michael Bewley joined Nearmap in 2017 as our first data scientist. Now the Senior Director of AI Systems, he leads the development of the Nearmap artificial intelligence product suite, quantifying the evolution of cities with the most superior AI data sets.
* 281 suburbs within greater Adelaide were included in the analysis, where Nearmap AI coverage exceeded 99%. The analysis includes each suburb where there is a minimum population of 1,000 residents (2016 census).
Analysis fundamentals
The fundamental aspects of the national study were reused for consistency. Key points include:
While we make every effort to ensure the accuracy of the data and analysis in blog articles, this information is not to be relied on as professional advice. No endorsement or approval of any third parties or their advice, opinions, information, products or services is expressed or implied by any information in the blog. Should you seek to rely in any way whatsoever upon this content, you do so at your own risk.
