Although the potential utilization of geographical information system (GIS) technologies in social service planning at a community level has been suggested for more than a decade (Chow and Coulton, 1997), actual applications are still rare. This article reports a pilot project in which service user records of a short term food assistance project operated in the East Kowloon region of Hong Kong are mapped and analyzed with the geographical information system. Spatial analysis of the data set reveals understanding of poverty problem in the region that may not be observed by using conventional statistical analyses. The project is suggested to have demonstrated a new method in locality service planning.

1. Locality service planning with GIS:
Spatial analysis of poverty data of
a community in Hong Kong
Zeno C.S. Leung, Lilian S.C. Pun-Cheng, Amy P.Y. Ho
Hong Kong Polytechnic University
Hong Kong, People Republic of China

2. About the Study
▪ spatially analyzing foodbank data in HK
▪ exploring community needs that may
not be observed by statistical analysis
▪ experimenting alternative method of
locality service review & planning

3. Background
▪ Poverty in HK
▪ Gini coefficient: 0.434, highest among
developed regions (UNDP, 2009)
▪ official poverty line defined in 2013: 1.31 million
(19.6%) before policy intervention, or 1.02
million (15.2%) after intervention
▪ Social security measures
▪ in cash: Comprehensive Society Security
Assistance Scheme (CSSA)
▪ in kind: food assistance being one of the kinds

4. Foodbank service
▪ officially as “Short-term FoodAssistance Service”
▪ 5 publicly funded and launched since Mar 2009;
reallocated as 7 in 2014;
▪ others foodbanks support by community resources
2014/7/13 4
(http://news.hkheadline.com/dailynews/photo_popup_for_dail
_news.asp?photoid=62897&news_id=72432)
Daily Meal Network
• one of the first five since 2009 , serving the
East Kowloon Region;
• rice & noodles, canned food; milk powder
for babies & toddlers, food coupons for
street sleepers; supermarket cash coupons
for chronic patients;

5. Dataset
▪ service recipients’ data collected by the knowledge
management system developed in 2009
▪ system effectiveness presented in HUSITA 2010
Case
Management
Food Warehouse
Management
Collaborative
Tools
MS SharePoint 2007 Application Server
MySQL MS SQL
Leung,Z.C.S., Cheung, C.F., Chan, K.T., & Lo, K.H.K. (2012). Effectiveness of Knowledge Management System in
Social Services - Food Assistance Project asAn Example. Administration in SocialWork, 36(3), 302-313.

6. GIS for social service
▪ geographic information systems (GIS)
▪ capturing, storing, querying, analyzing and displaying
geographically referenced data
▪ describes both the location and characteristics of spatial
features on the Earth’s surface (Chang, 2011: 1)
▪ discussion of potential applications in social services
since 1990s (Chow & Coulton, 1997; Queralt &Witte,
1998;Tompkins & Southward, 1999);
▪ experimented in different service areas such as children
(Ernst, 2000), homelessness (Wong & Hiller, 2001),
community health (Faruque et al., 2003), HIV related
(Kaukinen & Fulcher, 2006), immigrant service (Kim et
al., 2012);

7. Method & Processes
▪ 2,362 service recipient records (households,
representing 7000+ persons & 240,000+ meal-
days) from Mar 2009 to Aug 2010 (18 months)
▪ 2,344 valid addresses geocoded, i.e. translated
to local geographic coordinates;
▪ imported in and analyzed with ArcGIS
(http://www.esri.com/software/arcgis)
▪ discussed observations with social workers
involved in the service

8. Results & Observations

9. Service region, districts & sub-
districts
1:50,000
Population sub-district #
H 420,183 25
J 622,152 35
Q 436,627 24
* sub-district: district council election division;
often composed of 1 to 2 public housing estates
Regional population
1,478,962 (~21% of HK)
H
J
Q

10. R2
Blocks & Food distribution points

11. Distribution of service recipients
R2

12. Possibly under-served areas
Far from food
distribution points;
inconvenient / costly
transportation
Social workers’ comments:
• “not aware of it”
• “just know where they come,
but not where’s not coming”
• agreed further exploration
needed

13. Accessibility
Buffer
(in meters)
• “cost” - time or
transportation fees
• buffers of 400, 800 &
1200m plotted,
corresponding to approx.
5, 10 & 15 min walk
• no. of counts within
buffers (“points in polygon”)
R2

14. ▪ Social workers’ comments:
▪ “they would bring their trolleys”
▪ “some walked here and returned by bus”
▪ “some walked for more than 30 min., up and
down hill in order to save HK$12 (US$1.5)”
▪ S1 - “we have a mini-lorry for delivery, but
biweekly only”
▪ agreed alternatives to be explored, such as -
▪ food delivery by volunteers to elderly or disabled
▪ transfer case to closer distribution points
<400 800 1200 >1200
A1 12% 29% 36% 24%
C1 31% 29% 28% 12%
C2 52% 10% 24% 14%
K1 14% 24% 14% 48%
K2 85% 12% 1% 2%
K3 36% 24% 24% 16%
R1 36% 34% 24% 5%
R2 41% 13% 45% 1%
S1 19% 26% 22% 33%
ALL 39% 23% 19% 19%

15. Service take up rates
▪ counting “points in polygon”
▪ no. of households below poverty line in each sub-district
according to HK Census 2011
▪ take up rate =
𝑛𝑜.𝑜𝑓 𝑓𝑜𝑜𝑑𝑏𝑎𝑛𝑘 𝑢𝑠𝑒𝑟𝑠
𝑛𝑜.𝑜𝑓 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠 𝑏𝑒𝑙𝑜𝑤 𝑝𝑜𝑣𝑒𝑟𝑡𝑦 𝑙𝑖𝑛𝑒
× 100%
▪ μ = 2.11%; σ = 2.47%
▪ sub-district: take up rate (z-score)
H01: 1.84% (-0.11) H06: 0.86% (-0.51) H11: 1.67% (-0.18) H16: 1.16% (-0.39) H21: 0.47% (-0.66)
H02: 1.03% (-0.44) H07: 3.01% (0.37) H12: 0.78% (-0.54) H17: 2.44% (0.14) H22: 2.85% (0.30)
H03: 1.32% (-0.32) H08: 1.48% (-0.25) H13:0.70% (-0.57) H18:0.32% (-0.73) H23: 1.07% (-0.42)
H04: 2.68% (0.23) H09: 1.89% (-0.09) H14: 1.23% (-0.36) H19: 1.37% (-0.30) H24: 1.01% (-0.44)
H05: 1.34% (-0.31) H10: 2.51% (0.16) H15: 0.58% (-0.62) H20: 0.93% (-0.48) H25: 1.29% (-0.33)
H

16. Q01: 16.90% (5.99) Q07: 0.42% (-0.68) Q13: 0.95% (-0.47) Q19: 2.16% (0.02)
Q02: 4.03% (0.78) Q08: 1.86% (-0.10) Q14: 1.41% (-0.28) Q20: 0.79% (-0.54)
Q03: 1.50% (-0.25) Q09: 1.81% (-0.12) Q15: 2.33% (0.09) Q21: 1.63% (-0.20)
Q04: 2.27% (0.07) Q10: 1.56% (-0.22) Q16: 1.78% (-0.13) Q22: 1.88% (-0.09)
Q05: 3.03% (0.37) Q11: 0.71% (-0.57) Q17: 1.03% (-0.44) Q23: 2.70% (0.24)
Q06: 0.21% (-0.77) Q12: 0.87% (-0.50) Q18: 2.65% (0.22) Q24: 3.27% (0.47)
J01: 4.31% (0.89) J08: 0.64% (-0.60) J15: 1.82% (-0.12) J22: 1.50% (-0.25) J29: 2.07% (-0.01)
J02: 0.48% (-0.66) J09: 1.54% (-0.23) J16: 7.16% (2.04) J23: 2.19% (0.03) J30: 1.55% (-0.23)
J03: 6.30% (1.70) J10: 1.43% (-0.28) J17: 2.78% (0.27) J24: 1.89% (-0.09) J31: 3.40% (0.52)
J04: 0.51% (-0.65) J11: 1.46% (-0.26) J18: 13.95% (4.80) J25: 2.13% (0.01) J32: 3.66% (0.63)
J05: 1.93% (-0.07) J12: 1.02% (-0.44) J19: 6.79% (1.90) J26: 2.09% (-0.01) J33: 1.39% (-0.29)
J06: 1.12% (-0.40) J13: 0.36% (-0.71) J20: 0.87% (-0.50) J27: 0.97% (-0.46) J34: 1.20% (-0.37)
J07: 0.99% (-0.45) J14: 1.01% (-0.44) J21: 0.93% (-0.48) J28: 1.18% (-0.38) J35: 0.92% (-0.48)
J
Q

17. ▪ Social workers’ comments:
▪ J03 & J16 - old public rental housing (PRH) estates, more
poor elderly households expected
▪ J18 - not sure why for a newly redeveloped PRH; perhaps
more families of new arrivals
▪ J19 - surprised for a subsidized self-purchased estate, need
further exploration
▪ Q01 - fishing village, but also probably due to “word of
mouth” effect

18. Our learning

19. Visualizing
• what had happened
• what might not have
happened
• provide clues for
exploration
reasons of application employment status
district

20. Reviewing & Planning
• service needs, take-up or
drop-out rates, etc.
• users profiles - distribution,
clustering, etc.
• (re)defining localities,
service routes planning, etc.
• combine with other data
sources - census, health
care, delinquency rates, etc.

21. • easier to comprehend than
statistics
• to service provider as well
as service users / other
stakeholders
• promote community
participation and
empowerment
Collaborating

22. Next stage work
▪ limitations of the pilot
▪ residential addresses might not truly reflect their routes
▪ map distance ≠ geographical distance ≠ actual travelling distance
▪ put back layers of road & transportation networks for more
accurate analysis of cost & accessibility
▪ further analysis with respect to other demographic / social
attributes
▪ time dimension - data of 2014-15 to be collected, changes
or trends to be investigated
▪ explore the possibility of 3D analysis

23. Reference
Chow, J., & Coulton, C. (1997). Strategic Use of a Community Database for Planning and
Practice. Computers in Human Services, 13(3), 57-72.
Ernst, J. S. (2000). MappingChild Maltreatment: Looking at Neighborhoods in a
Suburban City. Child welfare, 79(5), 555-572
Faruque, F. S., Lofton, S. P., Doddato,T. M., & Mangum, C. (2003). Utilizing geographic
information systems in community assessment and nursing research. Journal of
community health nursing, 20(3), 179-191.
Kaukinen, C. & Fulcher, C. (2006). Mapping the social demography and location of HIV
services acrossToronto neighbourhoods. Health and social care in the community, 14(1),
37 -48.
Kim, C.K., Hong, P.Y.P.,Treering, D.J. & Sim, K. (2012).The changing map of
characteristics and service needs among Korean American immigrants in Chicago: A GIS-
based exploratory study. Journal of Poverty, 16(1), 48-71.

24. Queralt, M., &Witte, A. D. (1998). A map for you? Geographic information systems in
the social services. Social Work, 43(5), 455-469.
Tompkins, P.L. and Southward, L.H. (1999). Geographic Information Systems (GIS):
Implications for promoting social and economic justice. Computers in Human Services,
15(2-3), 209-226.
UNDP. (2009). Human development report 2009 - Overcoming barriers: Human mobility
and development.
Wong,Y.L., Hiller, E. (2001). Evaluating a community-based homelessness prevention
program. Administration in social work, 25(4), 21 -45