All the diagrams and maps from the analysis in this paper are embedded in a publicly accessible website using Plotly Dash. The website can be accessed through the following link: http://datashboard.com (accessed on 1 September 2022).
This study aimed to perform a spatiotemporal investigation of the developments in the CoS LGA from 2004 to 2022 to contribute towards filling in the gap of publicly available, comprehensive, data-based spatiotemporal analysis of development patterns in the CoS area. The results have shown several macro-level patterns in recent CoS development.
Development as a Sign of Socioeconomic Activity
Ruming (2009) noted that developments are unevenly distributed in NSW and taking 2005 to 2006 for example, Inner- and Middle-ring Sydney had approximately 1000 DAs per council 21. This is confirmed by the over 2000 entries found in CoS LGA for the same time period, which is double the average.
The research found that the number of DAs lodged has fluctuated over the years and followed a downwards trajectory since 2013, stabilizing in the last three years. Despite the shortage of data in 2022, a comparison with the same period in the past years so far shows that 2022 is likely to stay at the lower level in terms of DA lodgement. The analysis of total estimated cost of approved DAs shows a more complex pattern, with higher costs from 2013 to 2017 which may be the reflection of a recent concentration of development into larger, higher-cost projects. This is further reinforced by the steadily increasing median estimated cost of DAs approved each year.
The spatiotemporal analysis of development hotspots highlights a strong development push in specific suburbs (the Sydney CBD and Zetland), whereas other areas such as Glebe, Redfern or the northern part of Waterloo are cold spots of development. This result was consistent with a development intensity or DA count, as well as with a development value (estimated cost) analysis. Further investigation into the correlation between cold spots and social phenomena, in particular income levels and economic activity, could be a topic for future research.
DA Processing as a Constraint on Socioeconomic Progress
Approval time was argued by many as a key factor affecting housing affordability, as it reduces the potential pace of housing supply and therefore drives prices up 21. The NSW planning and development assessment system has seen major reform since 2005 21. A key part of this transformation was to decrease development assessment times and costs 21. Measures taken to achieve this included transferring planning power from local government to independent parties or state government, to remove local politics from the procedure 21.
Ruming (2009) found that average assessment time in Inner- and Middle-ring NSW LGAs was about 63 days, acknowledging that is probably due to the more complex investigation required on larger projects, rather than the result of intentional delay by government to hinder development activities 21.
The study of DA assessment time found consistent results and shows that the mean assessment time moved up further in 2015. The development process can still be a significant constraint, with more than 20% of developments requiring more than three months to be reviewed. On the bright side, almost 30% of developments are now reviewed under a month, and 4.24% under a week. While there is a positive correlation between estimated cost and assessment time, it was found that a significant number of high-value DAs were assessed with only short delays. Overall, 94% of developments are approved by the Council.
Further data would be required to understand the factors that influence the assessment time of specific DAs. One hypothesis to be explored in future research could be that sharing more development information as open data could result in better-prepared DAs and lessen the review load on the Council.
Evolving Uses as an Indicator of Changing Community Needs
New developments change the dynamics of the place, in particular when they introduce new uses. The research exhibited patterns of evolution in proposed use of new developments. Despite a relatively stable number of developments proposing new retail, residential and commercial uses, an upward trend of total estimated cost is seen from 2008 to 2019, with a stabilisation in 2020 presumably due to COVID.
As will be discussed below, the publicly available dataset offers insufficient information about current and evolving uses of developments, limiting insights in this area. The evolution of development uses may offer valuable insights for the community, as well as inform possible evolution of land zoning rules. This accentuates the need for the Council to release more detailed open datasets to the public.
Throughout the research process, a range of limitations were recorded due to constraints such as data availability, time and cost. These limitations are discussed below.
Improving Data Availability
In recent years, government agencies nationwide started to participate in the open data movement by digitising data and making them publicly available online. However, a large amount of data are yet to be digitised, standardised and reviewed before being made available. As a result, historical planning data such as previous LEPs are not yet available in GIS format. Digitising such data is a labour-intensive process for private individuals and is therefore not done for this research. Once such GIS data is made available, they can be added to the study to complete the picture.
Property Boundary Data Availability
Another difficulty faced in this research is the accurate mapping of DAs. DA location information is currently only available as addresses in text format. The dataset needs to be processed to have DAs allocated on maps with the right site polygon boundaries.
The initial design was to geocode the DAs before mapping the geocoded dot file on lot polygons from NSW SIX Maps. However, the free geocoding options demonstrated considerable accuracy issues, and were either limited in number of entries allowed, or very slow in processing. Google Geocoding API seemed to be more accurate, but credits are required to be purchased. After investigation, this method was deemed unsuitable given the time and cost constraints of this study.
The other approach was to find cadastre files with address information so that data can be directly joined by the address attribute. The only publicly available option found was the CoS current property boundary. However, lot boundaries change over time due to land amalgamation and subdivision. To most accurately map DAs from 2004 to 2022, all historical lot boundaries in this period are needed. The only historical boundaries found on AURIN are PSMA cadastre for 2016 to 2020. FMEWorkbench was first used to compare geometries between adjacent years to observe changes, but it picked up negligible deviations, making it challenging to identify real lot boundary changes. Manual comparison of 2016 and 2020 cadastres by colour overlay shows that changes in these four years are minor in the overall scale of CoS. Furthermore, it was noticed that even the latest PSMA cadastre has many small deviations from the CoS lot boundaries. As DAs are also Council information, it was decided that it is best to use Council property boundaries for this study.
To confirm that this deviation from best practice would not have a large impact on the accuracy of the analysis, earlier DA datasets, such as 2004, 2005 and 2006, were checked against the CoS properties boundaries. There were less than 0.5% of the DA entries that could not be matched with a lot boundary and most, if not all of them were issues with the address. It was therefore concluded that the selected approach is suitable for the purpose of this study.
In the future, should Council digitise more of the historical property boundaries, the study can be adjusted to utilise the cadastre from the respective year to further refine the results.
Existing and Proposed Land Usage
The proposed land uses in this study were inferred from the DA description field using word pattern matching based on a manual review of the data, as described in Section 3.4.5. This process lacks precision and can lead to incorrect interpretations of results. More detailed proposed usage data can be found in PDF attachments on individual DA webpages, but it would require considerable data scraping to gather, which was outside of the capacity of this study. As exemplified in the city monitor map, CoS likely already has a structured dataset of proposed usage information 28. Should Council add such information to this downloadable DA dataset in the future, the Python script can be updated to perform detailed proposed land use analysis.
This research designed a novel methodology for analysing individual DA data to extract spatial and temporal insights, making them available through a public dashboard. The research contributes towards filling the gap of publicly available, comprehensive, data-based spatiotemporal knowledge of development patterns.
With this case study of CoS DAs between 2004 and 2022, the approach focused on four research questions. The first two examined patterns of spatiotemporal distribution in terms of number of DAs or estimated costs. The third investigated patterns of evolution in development use such as residential, commercial and retail. The last question looked at assessment times and their possible influence on developments.
The study used in-depth Python analysis of CoS DA data combined with geographical property and suburb boundaries to produce charts and maps of macro-level development patterns. Moran’s I and spatial autocorrelation analysis helped highlight hot spots and cold spots at the level of individual suburbs. The analysis can be run again with updated datasets in the future to reveal further insights, making it a useful tool for other practitioners.
The results show a heavy development focus on specific suburbs such as the Sydney CBD and Zetland, both in terms of number of DAs and total estimated costs. Cold spots were identified in the west and the middle of the LGA, such as the localities of Glebe, Newtown, Erskineville, Eveleigh and Redfern, corresponding with a higher concentration of heritage conservation areas.
An investigation of new development uses highlighted rising costs across residential, commercial and retail developments, but insufficient information availability limited the depth of insights, especially when it comes to existing use which was missing from the dataset.
An analysis of DA assessment times across years and as a function of estimated cost showed a small but positive correlation between estimated costs and assessment time. It also indicated that although the majority of DAs are reviewed under three months, more than 20% require a longer time frame, which can be a significant constraint in the development process.
Broader Time Scale Investigation
As noted earlier in this paper, CoS DA information is currently available online from November 2004 onwards, limiting the scope of this study. Some of the older DA records (called Building Application or BA in earlier times) can be found at the CoS Archives & History Resources, where records assessed as being of permanent value are kept 63. These records currently have limited information and there is no automated way to download the full list of such applications. Once these older DA records are better digitised, they can be used to expand the study scope to provide a fuller review of the Sydney development history.
Building Height Compliance and Other Detailed Investigations
DAs contain considerably more information than what is available in this open dataset. Missing information includes proposed use category, building height, number of levels above and below ground, number of car parking spots and number of apartments, etc. As exemplified in Section 5.4.3, most of this information can be found in PDFs on individual DA webpages but requires extensive data scraping to gather it. If CoS adds the abovementioned information to this downloadable DA dataset, this study can broaden to provide spatiotemporal insights into areas such as building height compliance, residential density, housing typology distribution and car dependency.
Other Development Data
As mentioned early in this document, there are other types of developments in the CoS LGA, such as major projects. These large-scale projects, although not managed by the local government, often make a large impact on the LGA. Similarly, planning proposals that directly modify planning controls are another potentially interesting area of change to monitor in CoS. By combining these different pieces of the puzzle, a more complete picture of Sydney’s development pattern may be presented.
Wider Geographical Area
The process in this research is tailored to the data availability and format of the CoS LGA but has the potential to be adapted for other LGAs in NSW as well as other states. In fact, some local governments, such as Brisbane, directly provide proposed usage information 63. This means the data processing could be fast-tracked in such cases. This implies that by adjusting the Python scripting, this study has the potential to expand to national level. Higher-level patterns across state and territories can help compare development approaches Australia-wide.
This paper revealed clear spatiotemporal insights based on a limited dataset of DAs in the CoS area between 2004 and 2022. While this outcome could only be achieved thanks to the availability of open datasets, in depth insights were limited by the quality and structure of the data, and in breadth by the limited spatiotemporal scope.
This highlights the importance of coordination at higher levels of government to design shared, well-structured open data formats and to encourage all LGAs to adopt them. Such efforts would enable powerful visualisations of spatiotemporal phenomena and help engage the community throughout the planning process, further strengthening a productive collaboration towards a common goal.