Upload
others
View
5
Download
0
Embed Size (px)
Citation preview
Almost everything happens somewhere and in most cases, knowing where some things happen is critically important. Examples:
• Position of country boundaries• Location of hospitals• Routing delivery vehicles• Management of forest stands• Allocation of funds for sea defenses
Text source: Longley et al (2005) Geographic Information Systems and Science. 2nd
Edition. John Wiley and Sons Ltd. (Chapter 14, pages 317-319)
GIS
Geographical Information Systems are aspecial class of information systems that keeptrack not only of events, activities, and things,but also of where these events, activities, andthings happen or exist.Geographic location is an important attributeof activities, policies, strategies, and plans.Geographic problems involve an aspect oflocation, either in the information used to solvethem, or in the solutions themselves.
3GIS
Text source: Longley et al (2005) Geographic Information Systems and Science. 2nd
Edition. John Wiley and Sons Ltd. (Chapter 14, pages 317-319)
Snow was a British physician who is considered one of the founders of epidemiology for his work identifying the source of a cholera outbreak in 1854.
Father of GIS
5GIS
I. IntroductionII. Objectives of the StudyIII. Study AreaIV. MethodologyV. ResultsVI. Conclusions and Further Studies
7
The 2009 EFA Global MonitoringReport (UNESCO 2008) listed thePhilippines as one of the countrieswith decreased enrolment rate from 1999to 2006 having more than 500,000 outof school children. This trend ineducation indicators for monitoring thesecond MDG suggests that the countrymay probably not meet its target by2015.
8Introduction
Cohort Survival Rates (CSR*) for the past10 years have fluctuated between 60 %and 80 % in both elementary andsecondary levels (Department ofEducation, 2008).
This is alarming; education is a pre-condition to a country’s long-termeconomic growth (IIASA, 2008)
9Introduction
*the percentage of enrolees at the beginning grade or year in a given school year who reached the final grade or year of the elementary/secondary level
Tanauan City has been improving the quality of education from pre-school to high school.
As of 2009 there are already a total of 44 public elementary schools and 12 public secondary schools in the city. There are also 35 private educational institutions operating which caters elementary or secondary education or both.
11
Ordinary Least Squares[Spatial Statistics Tools of ESRI ® ArcMap™ 10.0]
› Performs global Ordinary Least Squares (OLS)linear regression to model a dependentvariable in terms of its relationships to a set ofexplanatory variables
› Spatial data often violates the assumptionsi.e. a stationary spatial process andrequirements of global models (e.g. OLS)
12Introduction
Spatial Data Characteristics
› Spatial autocorrelation : this creates anover-count type of bias for traditional(non-spatial) regression methods
› Non-stationarity : processes behavedifferently across the study area
13Introduction
Spatial Autocorrelation (Global Moran’s I)[Spatial Statistics Tools of ESRI ® ArcMap™ 10.0]
› Measures spatial autocorrelation based on feature locations and attribute values using the Global Moran's I statistic.
14Introduction
Geographically Weighted Regression (GWR)[Spatial Statistics Tools of ESRI ® ArcMap™ 10.0]
› a local form of linear regression used to modelspatially varying relationships
GOAL› To investigate the Socioeconomic factors that
influence the school enrolment rate in TanauanCity, Batangas
OBJECTIVES› Identify spatial patterns of households with
members not attending Elementary and HighSchool (Hot/Cold spots)
› Identify key socioeconomic factors thatinfluence school enrolment rate
› Model the relationship between schoolenrolment rate and its explanatory variables
15Objectives
The biggest numbers of children not in school were in Western Visayas, Central Luzon, and the Cavite-Laguna-Batangas-Rizal zone (Maligalig et. al, 2010)
The City of Tanauan had the biggest number of children not in elementary and high school in the Province of Batangas (Community Based Monitoring System, 2008-2010)
16Study Area
Maligalig, et. al, 2010. Education Outcomes In the Philippines, ADB Economics Working Paper SeriesCBMS, 2010. The Many Faces of Poverty in the Province of Batangas, Vol III
Land Area: 10,716 has 2nd Class City 48 Barangays Has the largest
population in the province
20Study Area
Year Total HHs
Total Pop’n
Children 6 – 12 years old
Children 6 –12 years old Not Attending Elementary
Children 13 – 16 years old
Children 13 – 16 years old Not Attending High School
2008 28,562 135,237 20,724 4,117 11,133 3,867
2011 32,472 149,691 21,954 3,983 11,776 3,322
Source: Community Based Monitoring System (CBMS) 2009 and 2011Data; Office of the City Planning and Development Coordinator,Tanauan City, Batangas
22Study Area
StatSim Pro 5.0
NRDB Pro
Database Access
23
Socioeconomic data and
Coordinates
Unique ID Code: BrgyPurokHCN
Formatted Tables
Python Script
Join Tablesin ArcMap
Socio-economic Data with
Coordinates
Point Shapefile for
all Households
Correction for Projection
I. Data Generation
Methodology
II. Spatial Pattern Analysis (Household Level)
Calculate Area Tool
•Study Area Boundary
Average Nearest Neighbor Tool
• HHs with Mem Not Attending Elementary/High School
Pattern (Clustered/Random/
Dispersed)
Assign Weights to HHs
*HHs w/ mem not enrolled :1
*No Mem 6-12/13-16:0
*All Mem enrolled : 0
Calculate Distance
Band from Neighbor Tool
*HH Point Location
Incremental Spatial
Autocorrelation*Distance BandNNO Observed
Hot/Cold Spot
Analysis*Distance Band
Threshold
24Methodology
III. Spatial Regression Analysis (Barangay Level)
25
Y = ß0+ ß1X1 + ß2X2 +… ßnXn + ε
Factor3
Factor10
Factor1
Factor6
Factor7
Factor8
Factor9
Factor4
Factor2Factor5
Scatter Plot Matrix and R-squared
Initially Identified
Explanatory Variables
OLS Summary & Diagnostics
Dependent Variable
EV4
EV3
EV2
EV1
EV6
EV5
Key Variables
GWR
OLS
Yi = ßi0+ ßi1X1 + ßi2X2+… ßinXn + ε
Global Model
Local Model
Methodology
Diagnostics
- Coefficient Determination- AkaikeInformation Criterion (AICc)- Adjusted R-squared- Joint F Statistic- Wald Statistic- Variance Inflation Factor (VIF)- Koenker’sBreusch-Pagan statistic- Jarque-Berastatistic
o Socioeconomic Factors Considered
• Number of HHs w/income below food threshold• Number of HHs w/income below poverty threshold• Number of HHs w/o access to sanitary toilet• Number of HHs w/o access to safe water• Number of HHs living in makeshift housing• Number of HHs who are informal settlers• Number of HHs w/ members who are College Graduates• Number of HHs w/ members victimized by crimes• Number of HHs w/ Unemployed member
26Methodology
HHs with Member/s Not Attending Elementary
HHs with Member/s Not Attending High School
CLUSTERED. There is 1% likelihood that the pattern could be the result of random chance
CLUSTERED. There is 1% likelihood that the pattern could be the result of random chance
Distance Band: 196.022Distance Band Threshold: 350NNO Observed: 13.43NNO Used: 15
Distance Band: 196.022Distance Band Threshold: 275NNO Observed: 13.43NNO Used: 15
28Results
30
Independent Variable
R-squaredDependent
Variable: Not Attending
Elementary
Dependent Variable: Not
Attending High School
Makeshift 0.228897 0.233102 Informal Settlers 0.605489 0.619895Unemployment 0.470770 0.481928Food Threshold 0.350858 0.379340Crime 0.626834 0.600158 College Grad 0.903331 0.869047 Safe Water 0.133439 0.178181Sanitary Toilet 0.110766 0.186226Poverty Threshold 0.649162 0.664245
Lowest R-squared:
MakeshiftFood ThresholdSafe WaterSanitary Toilet
Results
31Results
Makeshift Informal Settlers Unemployment Food Threshold
Crime College Grad Poverty ThresholdSanitary ToiletSafe Water
32Results
Makeshift Informal Settlers Unemployment Food Threshold
Crime College Grad Poverty ThresholdSanitary ToiletSafe Water
o Number of HHs w/ Unemployed membero Number of HHs w/income below poverty thresholdo Number of HHs who are informal settlerso Number of HHs w/ crime victims
33Results
Elementary
High School
o Number of HHs w/ Unemployed membero Number of HHs w/income below poverty thresholdo Number of HHs who are informal settlers
34
Adjusted R-squared: 0.915649
AICc: 440.603113
Elementary
High School
Adjusted R-squared: 0.925940
AICc: 408.414953
Results
35
# of Children Not Attending Elementary = 8.818879+ 0.400676 (# of HHs who are informal settlers) + 0.229829 (# of HHs Living Below Poverty Threshold) + 2.139997 (# of HHs with Crime Victims) + 0.251726(# of HHs with Unemployed member/s) + ε
# of Children Not Attending High School = 9.709695 + 0.457487 (# of HHs who are informal settlers) + 0.207117 (# of HHs Living Below Poverty Threshold) + 0.168162 (# of HHs with Unemployed member/s) + ε
Results
36
Elementary
High School
Adjusted R-squared (OLS): 0.915649Adjusted R-squared (GWR):0.918531AICc (OLS): 440.603113AICc (GWR): 443.370216
Adjusted R-squared (OLS): 0.925940Adjusted R-squared (GWR):0.946200AICc (OLS): 408.414953AICc (GWR): 404.938796
Results
HHs who are informal settlers
HHs who are living below
poverty threshold
HHs w/ Unemployed member/s
HHs w/ Crime Victims
38Results
39
HHs living below poverty threshold vs children not
in elementary
R-squared: 0.766258R-squared = 0.9523
HHs who are informal settlers vs children not in
elementary
HHs with members who are unemployed vs
children not in elementary
R- squared = 0.9809
Results
40
HHs who are informal settlers
HHs who are living below
poverty threshold
HHs w/ Unemployed member/s
Results
41
R-squared: 0.776088 R² = 0.6212
HHs living below poverty threshold vs children not
in high school
HHs who are informal settlers vs children not in
high school
HHs with members who are unemployed vs
children not in high school
Results
42Results
Elementary
High School
HHs Living Below Poverty Threshold is a stronger predictor for the number of children not attending high school compared to the number of children not attending elementary in most of the barangays in the east.
Informal Settlers
Informal Settlers
Poverty Threshold
Poverty Threshold Unemployment
Unemployment
The Poblacion-DarasaArea is a hot spot for both HHs with member/s not attending Elementary and High School
The barangays near the Taal Lake are also hot spots for HHs with member/s not attending High School
43Conclusions
The number of HHs living below poverty threshold, HHs with unemployed members, HHs who are informal settlers and HHs with crime victimes are the Socioeconomic factors that strongly influence the number of children not attending elementary
44Conclusions
The number of HHs living below poverty threshold, HHs with unemployed members, and the HHs who are informal settlers are the Socioeconomic factors that strongly influence the number of children not attending high school
45Conclusions
Living below poverty threshold is the most dominant factor influencing elementary enrolment rate in the indentified hot spot regions
Living below poverty threshold and informal settling are the most dominant factors influencing high school enrolment rate in the indentified hot spot regions
46Conclusions
The OLS model for the number of children not attending elementary is already sufficient
The GWR model for the number of children not attending high school is more robust than the OLS model
47Conclusions
Add Spatial Variable to the model (e.g. Distance to Schools)
Investigate the 2005 and 2008 socioeconomic data to check for trends and to further validate the model
48Further Studies
CBMS, 2010. The Many Faces of Poverty in the Province of Batangas, Vol III CBMS. 2008, Monitoring the Achievement of MDG ESRI ® ArcGIS™ 10.0 Help Library Book Bailey and Gatrell. 1995. Interactive Spatial Data Analysis. Pp 168-178,270-278 Brunsdont et al, 1998. Geographically weighted regression – modelling spatial
non-stationarity. The Statistician (1998) 47, Part3, pp. 431-443 Ericta and Fabian. 2009. A documentation of the Philippines’ Family Income
and Expenditure Survey. PIDS Discussion Paper Series No. 2009-18 Maligalig and Albert. 2008. Measures for Assessing Basic Education in the Philippines. PIDS Discussion Paper Series No. 2008-16 Maligalig et. al, 2010. Education Outcomes In the Philippines, ADB Economics
Working Paper Series No. 199 Nava,FJ. 2009. Factors in School Leaving: Variations Across Gender Groups,
School Levels and Locations. Education Quarterly, Vol. 67 (1), 62-78 Orbeta, A.C. 2005. Number of Children and their Education in Philippine
Households. PIDS Discussion Paper Series No. 2005-21
49
The authors would like to thank the following: Dr. Ariel C. Blanco and Prof. Oliver T. Macapinlac Ms. Nieves Borja and the staff of the Tanauan City
Planning and Development Office Engr. Nerio Ronquillo, Engr. Medel Salazar and the
staff of the Provincial Planning and DevelopmentOffice of Batangas
Ms. Celia Reyes, the project leader of the CBMS PEPPhilippines and staff.
50