Basic Needs Index

Basic Needs Index For Homelessness in Greater Sydney Region

Di Wen, dwen9655, 450036206 Husna Begum M R, husn2107, 500241114 Jocelyn Francis, jfra4343, 510414128 Prasoon Dhoundiyal, pdho6793, 490542495

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Table of Contents 1. Introduction 3 2. Indicator

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3. Indicator Table

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4. Demonstrating Application 7 5. Ranking Analysis

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6. Policy Reflection

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Reference

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Appendice

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1. Introduction With the rapid development of urbanization and increase of urban population, the “impoverished, homeless and displaced people” has increased and become a nightmare for urban decision-makers (Kriel, 2017, p.399). Homelessness is usually regarded as a “social and spatial problem” and raises “public policy concerns” in the recent trend (Amster, 2018, p.1). The problem is increasing significantly in developed countries, including Australia (Kriel, 2017, p.399). It is reported by ABS (2011, as cited in Healey, 2014, p.2) that “the homelessness rate rose by 20% or more in New South Wales”, and this number is increasing annually. According to ABS (2016a), homelessness groups are socio-economic disadvantaged people who are homeless and living in marginalized housing conditions. It is estimated that there are 58173 people in the homelessness category in the Greater Sydney region by 2016, which occupies approximately 1.2% of Greater Sydney’s total population.

It is revealed by Amster 2018 (p. 7-8) that homeless people are not passive victims. They make a lot of alternatives in domains as a fundamental of survival, which includes hygiene, food and shelter respectively. This statement is further supported by Kriel (2017, p.399) that those people are looking for a place to make money and stay safe.

By studying Maslow’s hierarchical pyramid of needs (refer to figure 1), Socio-economic disadvantaged groups, like homelessness, are identified to aspire to higher goals when their basic needs (physiological and safety needs) are met. The needs of homelessness people can relate to Maslow’s theory of human motivation. It provides a hierarchy of needs and often can be used as an assessment tool to reveal social issues (Poston, 2009, p.347).

Figure 1. Maslow’s hierarchical pyramid. Source: Bob Poston (2009)

During the rapid urban development process, the needs of the homelessness groups are likely to be ignored. Therefore, this report aims to particularly focus on the marginalized homelessnes group and understands the relative spatial distribution of facilities in Greater Sydney that would support their upliftment. The basic needs index would provide the policymakers with insights into infrastructure gaps for this targeted group. This report illustrates the Basic Needs Index through the visualized table, graph, and maps. In addition, this report also ranks the best and worst-performing areas in Sydney, which will further suggest a potential policy for improving the current situation.

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2. Indicator These infrastructures to the specified target group and particularly its implications on the marginalized groups like homelessness people is widely under-investigated. The basic need Index is developed to understand the spatial distribution of the basic facilities that would support the marginalized socio-economic group (hereafter mentioned as homelessness group). This report considers a family of indicators to support the Basic Needs Index. Sub-indicators in the family are identified by referring to Maslow’s hierarchical pyramid. The indicator family will include food index (cheap local groceries/eats), accommodation index (social shelters), humanity index (charity organizations to support the homelessness group), safety index (institutions to safeguard their safety and rights), which includes all probable facilities that would bring positive impact in their living conditions.

The developed indicators are limited to the availability of existing facilities information on the OpenStreetMap. These indicators can be better developed using a broader database that also includes strategic facilities planned at the local and regional levels.

3. Indicator Table Parent Indicator- Basic Needs Index Basic Needs Index Description

It measures the relative availability and accessibility of the food, accommodation, humanity and safety facilities for homelessness people, across Greater Sydney. The index is obtained by combining the values of the sub-indicators.

Rationale

Category of sub-indicators relates to the basic needs of Maslow’s hierarchical pyramid which must be satisfied first before homelessness people can reach higher aspirations

Sub-indicators

Limitations

Data Source

Frequency

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Food Index, Accommodation Index, Humanity Index, Safety Index 1) This index is a relative measure of accessibility of facilities per homelessness person and not an absolute measure.

2) Facilities accessed within 30 minutes are considered using the average peak-hour speed of public transport (bus) only (Bureau of Transport Statistics, 2016, p.2); and do not account for variations in transport type and frequency.

3) The facilities considered are not extensive databases.

Australian Bureau of Statistics : The ASGS shape file for SA3 Greater Capital City Statistical Area 2016 - the Greater Sydney (ABS, 2016b) Homelessness population count for SA3 region in 2016.

Open Street Map: Location of facilities (POI- Point of Interest) of all sub-indicators location) across Greater Sydney

Australian Bureau of Statistics: Once in five years

Open Street Map: Real-time Daily updates

Geography Extend

Uses the SA3 boundaries for estimating the number of homelessness populations.

However, the accessibility to facilities (POI- Point of Interest) within 30 minutes may include facilities (POIs) from neighbouring SA3’s too.

Method of Data Collection The count of homelessness population within the SA3 regions is taken from the “Estimating Homelessness Dataset” from the Table Builder (ABS 2016a). The location (Lat. long.) of the POI is scrapped from Open Street Map using the OSMnx library in python. The relative food security is calculated using the Food Index, where 30 minutes is defined as accessibility via public transport (bus).

Method of Analysis Basic Needs Index= (Food Index + Accommodation Index + Humanity Index + Safety Index) / 4

Method of Analysis Unit

Number

Sub-Indicator 1: Food Index Description: It measures the relative food insecurity issue for the homelessness people, across Greater Sydney, by measuring the availability of cheap food/grocery stores within 30 minutes of their location.

Rationale: The issue of food insecurity is usually measured for a region and not measured for individual population groups. Estimates show that nearly 4% - 13% of the general population has faced food insecurity issues in the past year depending upon their location (Commonwealth of Australia, 2020, p.2). This index draws attention towards the vulnerability of the homelessness people to food insecurity depending upon their location across Greater Sydney.

Limitations: In addition to the limitations mentioned in the above parent indicator, another limitation is that only major brands of cheap convenience stores, local greengrocers, farm & seafood foods are considered.

Data Sources, Frequency, Geography Extend & Data Collection: As mentioned in the above parent indicator Method of Analysis: Food Index = Proportion of cheap foods POI within 30 minutes of SA3 to Total number of cheap foods POI in Greater Sydney / Proportion of homelessness people within 30 minutes of SA3 to Total number ofhomelessness people in Greater Sydney Unit: Number

Sub-Indicator 2: Accommodation Index Description: It measures the relative availability of the social facilities for accommodating homelessness people, across Greater Sydney, by measuring the availability of social shelters, within 30 minutes of their location.

Rationale: Housing is a fundamental necessity recognized and supported through various key policies by the NSW government

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by providing social housing facilities for the target group. The index draws attention to the relative availability of social shelters for this target group across Greater Sydney.

Limitations: In addition to the limitations mentioned in the above parent indicator, another limitation is that indicator measures social shelters only and does not consider the supply of affordable rental housing and low-cost housing provided by the government.

Data Sources, Frequency, Geography Extend & Data Collection: As mentioned in the above parent indicator Method of Analysis: Accommodation Index = Proportion of social shelters POI within 30 minutes of SA3 to Total number of cheap foods POI in Greater Sydney / Proportion of homelessness people within 30 minutes of SA3 to Total number of homelessness people in Greater Sydney Unit: Number

Sub-Indicator 3: Humanity Index Description: It measures the relative availability of basic help (charities, foodbanks, second_hand shops) for the homelessness people, across Greater Sydney, by counting the number of such facilities, within 30 minutes of their location.

Rationale: NSW homelessness strategy informed by the NSW Department of Communities and Justice (DCJ, 2018, p. 7-8). identifies NGOs and charity groups as potential partners to address the problem of the homelesness target group to break the disadvantage cycle. The index draws attention to the relative availability of charity groups for this target group across Greater Sydney.

Limitations: In addition to the limitations mentioned in the above parent indicator, another limitation is that small shops of private non-profit groups may not be covered extensively.

Data Sources, Frequency, Geography Extend & Data Collection: As mentioned in the above parent indicator Method of Analysis: Humanity Index = Proportion of charity organizations (POI) within 30 minutes of SA3 to Total number of charity organizations POI in Greater Sydney / Proportion of homelessness people within 30 minutes of SA3 to Total number of homelessness people in Greater Sydney Unit: Number

Sub-Indicator 4: Safety Index Description: It measures the relative availability and accessibility of safety help (police station, clinic and public hospital) for the homelessness people, across Greater Sydney, by counting the number of such facilities, within 30 minutes of their location.

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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

4. Demonstrating Application 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.

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The visualisation below shows the proportion of total POIs across Greater Sydney in SA3 Regions:

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

Figure 3: The overlay of POIs with SA3 centroids and catchment area

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Fourth Step: Calculate and Segregate total POI counts into POIs for sub-indicators.

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

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5. Ranking Analysis The above developed Basic Needs indicators are used to rank the SA3 regions through various methods: Decile Ranking of Basic Needs Index: The SA3 regions of the Greater Sydney are ranked by using the Basic Needs Index in scale of 1-10; the lowest 10% areas are given value 1 and highest 10% areas are given value 10. The choropleth map clearly shows a higher Basic Needs Index in the inner suburbs around Sydney CBD and Northern Beaches, while the Basic Needs Index decreases towards the outer regions, which indicates the lack of overall facilities for the target group in the outer regions of Greater Sydney.

Figure 5: Basic Needs Index Ranking.

In order to observe the global autocorrelations, we have used Moran’s I statistic (Levine, N. 1999) It is a cross-product statistic between a variable and its spatial lag. The variable is expressed in deviations from its mean. For an observation at location i, this is expressed as zi = xi - x ̅ where x ̅ = mean of the variable.

Moran’s I statistic = I is described as:

where, S0=∑_i▒∑_j▒〖wij 〗 = sum of all weights n = number of observations The null hypothesis in Moran’s I test infers spatial randomness. The test can be based on normality assumption; however, such an assumption may produce erroneous results as discussed by Griffith (2005) and hence rather than employing normality, we have used a randomization technique. Under this technique, a reference distribution is created by randomly assigning the variable values over different locations. Then, reference distribution is used to calculate pseudo-p-value given as: p = (R+1)/(M+1) Where, R = Number of time the computed Moran’s I is equal to or more than the observed values, and M = the number of permutations.

We have calculated Moran’s I with 999 permutations and got the pseudo-p-value as 0.001, inferring that we can reject the null hypothesis and that Sydney is highly structured.

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Figure 6: Moran’s I statistical analysis: The distribution of values based on spatial randomness vs actual value (red)

The Morans scatter plot is used to observe the global auto-correlations. We observe that all the SA3 regions fall into quadrant 1 and 3, inferring positive spatial auto-correlation. Additionally, we do not have regions with significant pseudo-p-values that fall into the 2nd and the 4th quadrant, indicating negative spatial auto-correlation.

Figure 7: Global Auto-correlations: Moran’s I Scatter Plot

Then, we have looked at the local autocorrelations which develop a relationship between the sum of the local statistics with global statistics. It helps to identify the so-called hot spots bustling with amenities and cold spots deprived of facilities. As per the local Moran’s I plot we observe the Sydney;s hotspots agglomerated at CBD and as we move outwards we find cold spots with less number of facilities. Rest of the regions were statistically insignificant.

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Figure 8: Local Autocorrelations: Identification of hot spots (red) and cold spots (purple).

The spatial lag is a variable that averages the values of the neighbouring locations using a so called weight matrix and accounts for autocorrelations in a given model Murack (n,d.) Spatial lag is comparable to autoregression wherein we have a lagged dependent variable. Spatial weight matrix expresses a notion of geographical relationships between locations Smith (n,d.) and “Spatial weights in PySAL”. Weight matrix can be created based on many criteria such as contiguity, distance, combination of distance-boundary etc. We have used a contiguity based algorithm called Queens wherein it checks whether a polygonal shape in a graph shares an edge or vertex with another polygon. In comparison with Queen weights, Rook weights only consider if polygons share an edge. Hence, in further analysis we have employed Queens weights as we wanted to pull in information from adjacent polygons if they shared an edge as well as vertex or vertices.

Figure 9: Basic Needs Index Ranking (Spatial Correlation)

Decile Ranking (Spatial AutoCorrelation): Spatial AutoCorrelation algorithm (Griffith, 2005) is applied to Basic needs index across Greater Sydney to understand the systematic variation in the ranks.The observance of systematic variation with Spatial AutoCorrelation shows the positive correlation of one SA3 with its neighbours, indicating the ranking order of various SA3 regions are not spatially dispersed, but rather related to its neighbouring SA3 regions.

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The above developed Basic Needs indicators are used to rank the SA3 regions through various methods: Decile Ranking of Basic Needs Index: The SA3 regions of the Greater Sydney are ranked by using the Basic Needs Index in scale of 1-10; the lowest 10% areas are given value 1 and highest 10% areas are given value 10. The choropleth map clearly shows a higher Basic Needs Index in the inner suburbs around Sydney CBD and Northern Beaches, while the Basic Needs Index decreases towards the outer regions, which indicates the lack of overall facilities for the target group in the outer regions of Greater Sydney.

Ranking: Food Index

Ranking: Accommodation Index

Ranking: Humanity Index

Ranking: Safety Index

Figure 10: Ranking Map of Sub-indicators

Conclusion: In general, the spatial ranking of Basic Needs Index reveals the spatial disparity in the amenities for homelessness across Greater Sydney. It reveals that the accessibility of facilities as a phenomenon of inner suburbs only, have provided greater opportunities for the population in the inner suburbs to break out of their economic conditions. The analysis of sub-indicators shows food, accommodation & safety possessing similar trend to the parent indicator, but the variation in humanity index shows the spatial distribution of help homelessness groups across Greater Sydney.

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6. Policy Reflection The Basic Needs Index was developed to study the spatial distribution of basic facilities targeted for the marginalized homelessness group in Greater Sydney.

The ranking of SA3 regions clearly shows the disparity in spatial distribution, with populations living in inner suburbs having greater access to support facilities. Thus, it can be argued that having greater access to support facilities provides a better opportunity for the targeted community to break out from the economic disadvantage cycle.

In order to provide deeper insights, the proposed indicators and sub-indicators are compared with the existing SEIFA index of relative socio-economic disadvantage. SEIFA Index of relative socio-economic disadvantage measures all areas based on the income level, qualifications, and skills ABS (2016c). Areas with the lowest score composed of people with low income, qualifications, and skills.

Figure 11: Scatterplot for Index Comparisons

The scatterplot above shows a strong correlation between the developed index and the SEIFA IRSAD index. The scatterplot demonstrates that areas with relative socio-economic disadvantage groups (including our defined homelessness group) have lesser facilities to support their upliftment.

Although the developed indicators show a strong correlation, the sub-indicators show variations in the spatial distribution of various facilities. Hence, it can be argued that the developed index can be utilized as a monitoring mechanism to understand spatial lags in the support facilities. The developed index can be utilized to help build better infrastructure for the upliftment of the socio-economic disadvantaged groups, including the homelessness population.

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Reference ABS (2016a). Census Table Builder - Counting Persons, Estimating Homelessness. Retrieved from https://auth.censusdata.abs.gov.au/webapi/jsf/login.xhtml ABS (2016b). 1270.0.55.001 - Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas. Retrieved from https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1270.0.55.

001July%202016?OpenDocument ABS (2016c). 2033.0.55.001 - Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2016. Retrieved from https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by%20Subject/2033.0.55.001~2016~Main%20Features~IRSD~19#:~:text=The%20Index%20of%20Relative%20Socio,and%20households%20within%20 an%20area Amster, R. (2018). Lost in Space: The Criminalization, Globalization, and Urban Ecology of Homelessness. ProQuest Ebook Central. Retrieve from https://ebookcentral-proquest-com.ezproxy.library.sydney.edu.au/lib/usyd/reader. action?docID=837749&ppg=15 Australia Human Rights Commision.(2018). Homelessness is a Human Rights Issue (2008). Retrieve from https://humanrights.gov.au/our-work/homelessness-human-rights-issue-2008#6_3 Bureau of Transport Statistics. (2016). NSW and Sydney Transport Acts. Retrieve from https://www.transport.nsw.gov. au/sites/default/files/media/documents/2017/NSW%20and%20Sydney%20Transport%20Facts%202016.pdf Commonwealth of Australia. (2020). Understanding Food Security in Australia. (CFCA Paper No. 55). Sydney, Australia: Australian Institute of Family Studies. Retrieve from https://aifs.gov.au/cfca/publications/understanding-food-insecurity-australia#:~:text=In%20Australia%2C%20food%20security%20is,Indigenous%20population%2C%20depending%20on%20location.

Family & Community Services Housing (2013). Protocol for Homeless People in Public Places Guidelines for Implementation. Retrieve from https://www.housing.nsw.gov.au/__data/assets/pdf_file/0003/326046/ImplementationGuidelines.pdf Griffith, D A. (2005). Spatial Autocorrelation. Retrieve from https://www.sciencedirect.com/topics/computer-science/ spatial-autocorrelation#:~:text=Spatial%20autocorrelation%20is%20the%20term,together%20to%20have%20similar%20values Healey, J. (2014). Homeless people. ProQuest Ebook Central. Retrieve from https://ebookcentral-proquest-com.ezproxy.library.sydney.edu.au/lib/usyd/reader.action?docID=1126791 Kriel, JD. (2017). International responses to homelessness: Lessons for the City of Tshwane. DEVELOPMENT SOUTHERN AFRICA, 34 (4), 399–413. doi: https://doi.org/10.1080/0376835X.2017.1310027 Levine, N (1999). Development of a Spatial Analysis Toolkit for Use in a Metropolitan Crime Incident Geographic Information System. Retrieve from https://www.ojp.gov/pdffiles1/nij/grants/179282.pdf Local Government NSW. (2020). Submission to the Inquiry into the Protocol for Homeless People in Public Places.

Retrieved from https://lgnsw.org.au/common/Uploaded%20files/Submissions/Inquiry_into_the_Protocol_for_Homeless_People_in_Public_Places.pdf Murack, J. (n,d.). Regression Analysis Using GIS. Retrieve from https://libraries.mit.edu/files/gis/regression_presentation_iap2013.pdf

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NSW Department of Community and Justice. (2018). NSW Homelessness Strategy. Retrieved from https://www.facs. nsw.gov.au/__data/assets/pdf_file/0007/590515/NSW-Homelessness-Strategy-2018-2023.pdf NSW Department of Planning, Industry and Environment. (2021). Homeless people policy. Retrieved from https:// www.environment.nsw.gov.au/topics/parks-reserves-and-protected-areas/park-policies/homeless-people Poston, B. (2009). Maslow’s Hierarchy of Needs. Retrieved from https://www.ast.org/pdf/308.pdf Smith, TE. (n,d.). Spatial Weight Matrices. Retrieve from https://www.seas.upenn.edu/~ese502/lab-content/extra_ materials/SPATIAL%20WEIGHT%20MATRICES.pdf

Appendice Refer to all supporting Working Files Submitted 16