4. Demonstrating Application

Rationale:

The physical safety and security of homelessness people is under threat, especially for the vulnerable group including children, youth, and women of homelessness (Australia Human Rights Commision 2018). Besides, according to Family & Community Services Housing (2013, p.7) NSW Police Force and medical treatment should be provided to meet the safety and security needs of the homelessness.

Limitations:

Limitations as mentioned in the above parent indicator

Data Sources, Frequency, Geography Extend & Data Collection: As mentioned in the above parent indicator

Method of Analysis:

Safety Index = Proportion of safety support (POI) within 30 minutes of SA3 to Total number of safety support POI in Greater Sydney / Proportion of homelessness people within 30 minutes of SA3 to Total number of homelessness people in Greater Sydney

Unit: Number

Basic Need Index for homelessness population is developed by measuring the relative availability of basic needs facilities across Greater Sydney.

First Step: Data Mining – Scraping Points of Interests (POI)s

The relative availability of basic needs facilities is measured by scraping the POIs from the Open Street Map using the OSMnx library in Python.

Metadata of the scraped POIs consist of the types of POIs (key-value pairs), latitude and longitude, respectively. The metadata was saved in the geo package and XML Graph format —[Images of Scraped Data is attached on Appendix].

Second Step: Data Cleaning – extracting relevant POIs

The raw data that was scraped from the Open Street Map was further cleaned using the description and names tags of metadata to retain the relevant POIs.

Food Index is composed of affordable local stores and groceries, such as convenience stores, local greengrocers, local farms, and seafood POIs. Accommodation Index is composed of social shelters facilities POIs. Humanity Index is composed of NGOs, Charity Organisations and Second Hand Shops. Safety Index is composed of Public hospitals, police stations, and Clinic POIs.

Then, these extracted POIs were converted from polygons to points using pyproj library.

Figure 2: The proportion of total POIs across Greater Sydney

Third Step : Creating accessibility regions within 30 minutes.

The SA3 Greater Capital City Statistical Area 2016 ASGS Shapefiles was imported using geopandas library. The shapefile was converted into its centroid points using CRS from the pyproj library (epsg=3857 projections system has been used for conversion). The number of POI available within a 30 minutes accessibility of the SA3 region is considered POI available for the respective SA3 region. While considering the 30 minutes accessibility, the average speed of public transport (bus) in peak hours (36kmph) is considered for calculating the catchment radius, which translates to 18kms radius around the centroid of SA3 (Bureau of Transport Statistics, 2016, p.2) .

The visualisation below shows the overlay of POIs with SA3 centroids and catchment area:

Location of facilities

Centroid of SA3 areas

30 mins Radius

The Greater Sydney Region

The total POI counts for each region is calculated and segregated into POIs for sub-indicators. The POIs number within the catchment area of each SA3 is counted as unique instances for each SA3 region. The final value is appended to the SA3 Shapefile. Furthermore, the POIs are segregated to calculate the food, accommodation, humanity and safety indicators.

Fifth Step: Calculating of Indicator and sub-indicator

The count of POIs is used to calculate the indicator using the formulae in indicator table The bar graph below shows the variation in the Basic Needs Indicator for all SA3 region, together with the sub-indicators:

Figure 4: bar graph of the Basic Needs Indicator for all SA3 in the Greater Sydney region