CSIS Discussion Paper No. 139

Developing a Prototype of Geospatial Data Sharing andAnalysis System for Disaster Management in the Philippines:
A Case Study on Typhoon Haiyan (Yolanda)

Yuichi S. HAYAKAWA 1 , Takashi OGUCHI 1 , Hill H. KOBAYASHI 1 ,Rene MENDOZA 2 , Marjon N. De PAZ 2 , Jeanette D. BADONG 2 ,Toshikazu SETO 1 , Yasuhisa KONDO 3May 2015

1 Center for Spatial Information Science, The University of Tokyo, Japan
2 Knowledge Management Division, Advanced Science and Technology
Institute, the Philippines
3 Center for Research Promotion, Research Institute for Humanity and Nature,Japan1AbstractFollowing the severe disaster by typhoon Yolanda (Haiyan) in November 2013,
we constructed a web-based geospatial database system for disastermanagement in the Philippines. Focusing on meteorological data, particularly
those for heavy rainfalls, obtained from sensor network deployed in thePhilippines, we developed a prototype of a geospatial database based on aweb-based, open source GIS (geographical information systems), to facilitatesharing and visualization of various geospatial data concerning both naturaland social environments. In this system, desktop GIS is effectively used forperforming geospatial analyses, while our assessment on data transfer throughtheinternet

revealed

thenecessity

ofsolvingthenetwork-andhardware-related problems. The prototype system developed in this study will
be further enhanced for practical applications to the mitigation of natural
disasters in the Philippines.
Keywords: data sharing, spatial data visualization, natural disasters,
Philippines21. Introduction
The islands of the Philippines suffer from frequent natural disasters
including heavy rainfall, floods, landslides and severe winds related totyphoons (e.g., Boquet, 2015; Brassard et al., 2015), along with volcanic andseismological disasters. For disaster mitigation in such developing countries,
construction of a nationwide climate monitoring system and a real-time
emergency alert system is an urgent issue. This study aims to complement
existing projects regarding a meteorological monitoring system for thePhilippines operated by DOST-ASTI (Advanced Science and TechnologyInstitute), and develop related applications by providing technology that will
enhance the observation, collection, and transmission of environmental data
by CSIS (Center for Spatial Information, The University of Tokyo). Focusing
on meteorological data from numerous sensors with special attention to heavy
rainfall, we constructed a geospatial database including various spatial data
concerning both natural and social environments. We also developed aprototype of data sharing and visualization systems using a WebGIS platform,
which effectively connects the web-based geospatial database and desktop
GIS. Preliminary spatio-temporal analyses for disaster mitigation were also
performed using the geospatial datasets.

2. Materials and Methods
2.1. Meteorological Monitoring System in the Philippines
Here we explain the existing system for climate monitoring in thePhilippines. The objectives of the construction of this system by ASTI are:1) to produce locally developed instruments for weather monitoring andforecasting, and2) to develop cost-effective platforms and applications for real-time data
gathering of environmental parameters.
ASTI utilizes low-cost instruments for the nationwide monitoring system.
Advantages of this system over commercially available instruments are: much3

lower finantial cost for the same or even higher performance; locally available
technical support; and utilization of local suppliers of components and parts.
The sensor deployment status is summarized in Table 1.
The first related project is under the Hazards Information Media, a projectfor disaster management using WebGIS. This project consolidates geospatial
data with the end output. This will be a complete online hazard and disaster
decision support system accessible to the public through web browsers andmobile applications. This web portal shall provide visualization of geospatial
data with real-time high resolution hazards and disaster-related information.
Data from the other component projects under the NOAH program, includig
flood maps, 3-D mapping of major river basins and point data from hydromet
sensors, other weather stations from other projects, and the probability of rain,
will be displayed in this portal.
The data collection started in 2011. For data sharing, the downloadable
data are provided as CSV format. The CSV data are available through a
website (http://repo.pscigrid.gov.ph). Some directories are restricted forspecific users requiring a username and password. APIs are also available if
one requests for formal approval of accessing. The available data include thelist of installed meteorological stations, all data of the meteorological stations
and historical data, which are provided in the JSON format.
Through the operation of the system, several issues have been found to befurther improved. The security of the stations in the field is most crucial: If
left unsecured, the stations will be vandalized, or at worst stolen and sold for
scrap metal. Also stations are being used as homes for insects and bats. The
necessity of backups is another important issue. Keeping backups foreverything is necessary not to lose the real-time monitoring data. For this,
twofold network communications are applied: SMS and satellite networks (Fig.
1). Furthermore, servers in highly redundant configuration is necessary. Theseshould include RAID disk configuration and multiple server setup for
implementation of best practices for system architecture. Communications inthe community is also important for sustainable management and operation ofthe system.

42.2. Construction of a Web-GIS Server and Applications to Spatial Analysis
Following the current state of the meterological sensor network for thePhilippines described above, CSIS has provided help with geospatial analysis
on sensor data especially on Typhoon Yolanda (Haiyan), network performance
analysis and recommendations for improvement, and recommendations on thecurrent GIS architecture to implement a prototype of geospatial data sharing
systems. For this purpose, we carried out 1) network performance assessment,2) offline experiment using sensor data, 3) development of a WebGIS system,and 4) spatial data visualization and analyses using the sensor data and othergeospatial data layers incorporated in the WebGIS.
As a performance assessment, experiments were carried out regarding thedirect connection from the CSIS network system to the ASTI data server using
API (Fig. 2). The meteorological sensor data provided as CSV files were thenprocessed to be incorporated in GIS software.
We utilized GeoServer for the basic system of the WebGIS. Geosptial
datasets were integrated into the WebGIS, including the meterological data by
ASTI and other open data: OpenStreetMap (road, building, boundaries),
population data, Yolanda typhoon data, and topographic data (SRTM digital
elevation model). People Flow 1996 Manila Metropolitan Area provided by
CSIS People Flow Project Office (Sekimoto et al., 2011), comprising a
population of ca. 189,000, were also incorporated. Geospatial analyses werealso performed using the spatial datasets listed above and desktop GIS (QGISand ESRI ArcGIS).

3. Results and Discussion
3.1.Experiment on the server connection
The result of the performance assessment of direct connection between
CSIS and ASTI is shown in Fig. 3. The response time of ASTI server from/tothe ASTI network seemed to be short and stable, while that from/to CSIS
showed large delay (3 to 4 times than that within ASTI network) of the datatransfer. Large jitter was also observed, causing the delayed packets of data5transfer. We conclude here that real-time data transfer between CSIS andASTI and concurrent analyses are currently impracticable due to the delay ofdata transfer. Further improvements in the network infrastructure and thestrengthening of related hardware including servers would be necessary tofulfill real-time data utilization.
3.2. CSV data formatting for offline experiments
The meteorological sensor network provides more than 700 CSV files ofthe sensor data everyday. Although the data format is in CSV, the originalcomponent of the files are complicated and hard to be used in GIS. The data
therefore need selection and reformatting for geospatial analyses. We selected
and reformatted CSV files for sensor data having rainfall and air pressure data,
giving 201 records per day.
Even after the reformatting, some error removal was necessary. The maincauses of the errors are the lack of location data of several sensors,
duplication of sensor data obtained, and erroneous outlier values of the sensor
data. Such errors are supposed to be derived from unexpected inaccurate
values from the original sensors, as well as the loss of data packets during datatransfer from the sensor to the storage server.
3.3. WebGIS development
Fig. 4 shows a snapshot of the administration page of the web-based GIS
database using GeoServer. This system is capable of distributing map layer
data to local (desktop) GIS software via the Internet, as well as map
visualization of the data layers on the web browsers. Once the spatial data areretrieved to the desktop GIS (Quantum GIS, QGIS) of approved users, spatial
analyses that are available in the desktop GIS modules can be performed in thelocal machine (Fig. 5). The results of such analyses can also be uploaded tothe server. The data layers in the WebGIS can therefore be enriched by various
kinds of analyses performed on user desktop machines.
An advantage of this system using both the WebGIS and the desktop GIS is
flexible spatial data analyses and visualization. Because the ability of spatial
analyses on server-based GIS strongly depends on the performance of the6server and network connection, the time for data processing can be reduced
when the analyses are performed on the desktop GIS.
3.4. Visualization and spatial analyses of the data
Using the sensor data and the other spatial datasets, we carried out spatial
data visualization and analyses. Fig. 6 shows a basemap of the sensor network
located in the Philippines.
As an example, maps of hourly rainfall on the day of typhoon Yolanda(Haiyan) on November 8, 2013 were created (Fig. 7). The point data of hourly
rainfall obtained by each sensor were interpolated using the kriging
interpolation method (ordinary kriging, circular function, 12 neighborhoods).
The rainfall variability is relatively moderate due to the interpolation. The
central part of the Philippine islands shows larger rainfall values, which isconsistent with the path of the typhoon (Fig. 5).
The rainfall data were further summarized and categorized into two classesof daily rainfall for the day of typhoon Yolanda, and put over the topographic
data showing areas with slope gradients larger than 30 degrees (Fig. 8). Suchslopes steeper than 30 degrees (approximation of the angle of repose) aresupposed to be prone to landslides. The areas with steep slopes and highamounts of rainfall are shown as potential landslide hazard areas. The
relationship between topographic slope and rainfall was also examined (Fig.
9). The result indicates that the moderately sloping areas (4–12 degrees) havemore rainfall, and the spatial distribution of such areas shows a higher
potential of flood disasters where lots of people may live.
Spatial relationships between local population derived from the people
flow data and the typhoon rainfall were compared in the Manila area (Figs. 10,11). Although the rainfall amount due to the typhoon Yolanda was not so
critical in the Manila area, the concentration of population in the centralManila, where a large river is located, indicates potentially higher risk offlooding under heavy rainfall (Fig. 11). In fact, a heavy rainfall event occurred
around Manila area around August 20, 2013, caused by both a typhoon and amonsoon rain front (Lagmay et al., 2015). Characteristics of the rainfall event(mid-late August, 2013) and that by typhoon Yolanda (early-mid November,72013) were compared (Fig. 12). The hourly rainfall and the soil water index(SWI; Okada et al., 2001) of the station at Sangley Point, located 14 km
southwest of the center of Manila, show different characteristics of the rainfall
events. As noted, the rainfall by typhoon Yolanda was not critical, while thatin August resulted in high SWI values indicating a high risk of flooding in thelowland area around Manila.

4. Concluding remarks
Combination of the meteorological sensor network system operated by
DOST-ASTI and the WebGIS system developed by CSIS looks promising for
future hazard mitigation in the Philippines, because it enables various GIS
applications and geospatial analyses. It also became clear that network-related
problems have to be solved for further developing more practical real-timehazard assessment and alert systems.

Acknowledgments
This study was supported by the Japan-Philippine Urgent Collaborative
Projects regarding Typhoon Yolanda (Haiyan) within the J-RAPID Programprovided by the Japan Science and Technology Agency.

References
Boquet, Y., 2015, Metro Manila’s Challenges: Flooding, Housing andMobility, in Singh, R.B. ed., Urban Development Challenges, Risks andResilience in Asian Mega Cities, Springer, p. 447–468.
Brassard, C., Giles, D.W., and Howitt, A.M., 2015, Natural Disaster
Management in the Asia-Pacific: Springer.

8Lagmay, A.M.F., Bagtasa, G., Crisologo, I.A., Racoma, B.A.B., and David,C.P.C., 2015, Volcanoes magnify Metro Manila’s southwest monsoon
rains and lethal floods: Frontiers in Earth Science, v. 2, doi:10.3389/feart.2014.00036.
Okada, K., Makihara, Y., Shimpo, A., Nagata, K., Kunitsugu, M., and Saitoh,K., 2001, Soil Water Index (SWI): Tenki, v. 48, no. 5, p. 349–356.
Sekimoto, Y., Shibasaki, R., Kanasugi, H., Usui, T., and Shimazaki, Y., 2011,
PFLOW: Reconstruction of people flow recycling large-scale social
survey data: IEEE Pervasive Computing, v. 10, no. 4, p. 27–35.

9Table. 1. Sensor deployments in the Philippines (as of March 2015).	

Type of Station

Parameters Measured

Quantity

ARG

Rainfall, air pressure

686

WLMS

Water level

332

TandemWater level, rainfall, air pressure

134

AWS

Rainfall, rain intensity, rain duration, airpressure, temperature, humidity, wind speed/
direction

111

AgrometSame as AWS + soil moisture, soil
temperature, sunshine duration, sunshine
count, solar radiation

8010

Satellite Service Provider
via satellite

web portal

via SMS
servers

Fig. 1. Network transfer of meteorological sensor data.	
11

CSIS+ASTI!connec0on
xxxxxx

Internet!!!!!!!xxxxxx.ASTI+ASTI!
connec0on                      …
…

Fig. 2. Schematic illustration of the network connection between CSIS and ASTI.	
12

20Time-(s)

1816

CSIS$ASTI

1412108

ASTI$ASTI64201357911 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Number-of-s

Fig. 3. Performance assessment of direct connection between CSIS and ASTI
and that within ASTI.	
13

(a)	

(b)	

(c)	

Fig. 4. Snapshot view of an administration page of the web-based GIS using GeoServer.
(a) Table view of the layers stored in the GeoServer system. (b) Map view of point locationsof the sensors using OpenLayers. (c) Map view of country boundary of the Philippines.	
14

Fig. 5. Snapshot view of desktop GIS in a local machine showing the data
retrieved from the WebGIS server. Green points indicate locations of the meteorologicalsensors. Black lines indicate the boundary of the Philippines. Blue line shows the track oftyphoon Haiyan (Yolanda). Background grayscale image is the elevation derived from 90-mresolution SRTM DEM.	

15

Fig. 6. Basemap showing the sensor network in the Philippines.	
16

01
AM	02
AM	

03
AM	04
AM	

05
AM	

06
AM	

07
AM	

08
AM	09
AM	

Fig. 7. Spatial distribution of hourly rainfall on November 8, 2013.	

17

10
AM	

11
AM	00
PM	01
PM	02
PM	03
PM	04
PM	05
PM	06PM	Fig. 7. (cont.)	

18

07PM	08PM	

10PM	

11PM	09PM	

hourly rainfall		-2
10246810121416182022mm

Fig. 7. (cont.)	
19daily rainfall(mm)

slope >30°

Fig. 8. Overlay of daily rainfall distribution and steep slope areas,
showing potential landslide hazard areas.	
20daily rainfall(mm) mean ±1

number of cellsof land area

slope (deg)
relatively higher rainfalls in slopes 4-12 degs

slope = 4-12°

daily rainfall(mm)

Fig. 9. Relationship between topographic slope and daily rainfall.	
21

population (/km2)

(source: person trip data
1996 Manila by CSIS-i)

hourly rainfall (mm)
13.9
0

Fig. 10. Time series of typhoon rainfall and local population derived from people flow data.	
22hourly rainfall (mm)

population (/km2)
only >20k/km2

(b)	

2.0
0.7

(a)	

population
density (km-2)	

rainfall(c)	 hourly(mm)	

(d)	

12:00
17:00

08:00

00:00

distance from the center of Manila (km)	

distance from the center of Manila (km)	

Fig. 11. Local population and rainfall by typhoon Yolanda in relation to the distance toManila Center.
(a) Euclidean distance from center of Manila. (b) Magnified view of central Manila showing
populated areas (>20,000 km-2) and hourly rainfall. (c) Relationship between the distancefrom the center of Manila and population density for each time band. (d) Relationshipbetween the distance from the center of Manila and hourly rainfall for each time band.	
23111512

111500

83112

83100

83012

83000

SWI

100
10050
150

0100
SWI5080100

60

401500Fig. 12. Example of temporal changes in hourly rainfall and the soil water index
at station SANGLEY POINT in Cavite, near Manila center.
(a) Aug. 15-31, 2013. (b) Nov. 01-15, 2013.	
hourly rainfall (mm)

50

hourly rainfall (mm)

(b)	

111412

82912

82900

82812

82800

82712

82700

82612

82600

82512

82500

82412

82400

82312

82300

82212

82200

82112

82100

82012

82000

81912

81900

81812

81800

81712

81700

81612

81600

81512

81500

SWI

(a)	

111400111312111300111212111200111112111100111012111000110912110900110812110800110712110700110612110600110512110500110412110400110312110300110212120110200

140

110112110100

SWI
150
0

hourly rainfall

200

time (month-day-hour)
0hourly rainfall

20

200

time (month-day-hour)

24