Developing a Geospatial Framework for Calculating a 15-Minute

City Index (FMCI): The Case of Quezon City

Carlo Angelo R. Mañago

, Marielle G. Nasalita

, Cesar V. Saveron

, Ynah Andrea D. Sunga

and Alexis Richard C. Claridades

e,*

Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City, Philippines

Keywords: 15-Minute City, Urban Life Quality, 15-Minute City Index, Service Area, Network Analysis, Pedestrian

Network.

Abstract: The 15-minute city concept is a measure of the quality of urban life based on proximity, sustainability, and

sociality. This study proposes a geospatial framework for calculating the 15-minute city index (FMCI) aimed

to measure the accessibility of its residents to six social functions, which include living, working, supplying,

caring, learning, and enjoying. Quezon City, Philippines, was chosen for its urban characteristics that aligned

with this vision and served as the study area. To account for pedestrian needs, age-based weights were

assigned to the social functions, and service areas were mapped using a uniform walking speed. FMCI values

were calculated based on weighted social functions and barangay population distribution by age group.

Results revealed that 39% of Quezon City’s barangays achieved a perfect FMCI score of one, indicating

access to all six functions within a 15-minute walk. Positive spatial autocorrelation indicated the clustering of

barangays with similar FMCI values, with hot spots in the southern and cold spots in the northern parts of the

city. These findings offer insights for policymakers in improving urban life quality. The adaptable FMCI

framework can be applied to other urban areas to assess service accessibility, considering residents' needs.

1 INTRODUCTION

The 15-minute city is a population-oriented urban

development concept that has gained popularity in

recent years. Its primary idea is that the accessibility

to essential services of the residents should be within

a 15-minute radius of their homes, whether by

walking, biking, or utilizing public transportation

(Poorthuis & Zook, 2023). It is a multi-reformative

urban model that addresses the decentralization of a

city, reduction of carbon footprint, and social and

economic integration (ArchDaily, 2024). The

COVID-19 pandemic brought renewed focus to this

pedestrian-centered idea, as lockdowns underscored

the need for easily accessible amenities amid

limitations on mobility and public transportation

(Akrami et al., 2024).

https://orcid.org/ 0009-0001-7799-1645

https://orcid.org/0009-0001-1777-7161

https://orcid.org/0009-0006-0492-4608

https://orcid.org/0009-0008-5763-5070

https://orcid.org/0000-0001-9826-4271

*Corresponding author.

Moreno et al. (2021) claim that the ability of

residents to access six essential urban social functions

(SF), which include (a) living, (b) working, (c)

supplying, (d) caring, (e) learning, and (f) enjoying,

in a 15-minute space-time frame is linked to a higher

quality of life. This concept connects the proximity

and integration of the six essential functions to the

enhancement of well-being, sociality, and

sustainability, which emphasizes the purpose and

significance of the 15-minute city to offer a high-

quality urban life to the residents.

For the practical and effective implementation of

the 15-minute city, each societal function should be

broken down into subcategories and linked to

corresponding specific uses, activities, and facilities

(Moreno et al., 2023). Table 1 provides a summary of

the elements and facilities associated with six SFs.

Mañago, C. A. R., Nasalita, M. G., Saveron, C. V., Sunga, Y. A. D. and Claridades, A. R. C.

Developing a Geospatial Framework for Calculating a 15-Minute City Index (FMCI): The Case of Quezon City.

DOI: 10.5220/0013426100003935

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2025), pages 73-84

ISBN: 978-989-758-741-2; ISSN: 2184-500X

Considerably, population distribution per age

distribution of a city is an important demographic

characteristic to be considered in assessing a 15-

minute city (Moreno et al., 2023). For instance, a

child must have access to learning or enjoying SFs,

while the elderly must have access to the caring SF.

Assessment of accessibility within the neighborhood

requires incorporation of the origins of mobility

needs, which the population distribution represents.

This ensures that services are located close to the

people who are the users of these services (Caselli,

2021). Thus, this research project will incorporate

population data to facilitate the assessment.

Table 1: Elements and facilities are integrated into the six

SFs.

Social Function Subcategories

Living

Housing

Energy

Waste Management

Transportation

Services

Infrastructure

Working

Environment

Access

Diversity

Services

Getting Supplies

Food

Non-food-related consumption

Public services

Enjoying

Holidays

Culture

Leisure

Association

Learning

Access

Availability

Performance

Guide

Caring/Being Healthy

Access to care

Prevention

Emergency

Living environment

Wellness

Sport

Pollution

Over the past few years, the 15-minute city

concept has gained significant support from various

countries due to its urban planning benefits. Cities

such as Barcelona, Oxford, and Paris have embraced

this model (Vaughn, 2023). In China, the government

has prioritized the development of walkable

neighborhoods, ensuring that essential public services

are accessible within a 15-minute walk. This initiative

not only promotes walking behaviors and enhances

overall health but also highlights the critical role of

such neighborhoods in reducing the risk of obesity

and non-communicable diseases (NCDs) among

residents (Weng et al., 2019).

Scholarly research demonstrates varying

approaches to measuring accessibility in urban

contexts, with some studies favoring network

distances (Birkenfeld et al., 2023; Fazio et al., 2023;

Gorrini et al., 2023; Knap et al., 2023) while others

employ Euclidean distances, largely due to the

computational challenges posed by road network data

(Gaglione, 2022; S. Zhang et al., 2022). Network

distances are generally preferred as they provide more

realistic and precise results by reflecting the actual

paths residents travel to access urban amenities

(Papadopoulos et al., 2023). In contrast, Euclidean

distances are deemed inadequate for spatial analyses

within road networks, as they fail to account for

critical factors such as road length and travel time,

leading to underestimation (Shahid et al., 2009).

A variety of indicators have been used to evaluate

the principles underlying the 15-minute city concept.

For example, Starrico (2022) assessed service

accessibility by calculating the percentage of the

population with access to at least one amenity of the

same type within a 15-minute walking radius,

highlighting the need to consider population

coverage. The selected metrics aid in optimizing the

balance between service count, spatial distribution,

and area coverage relative to user locations. Starrico’s

findings revealed significant disparities, with certain

areas over-supplied with amenities while others

lacked sufficient services despite being populated.

Similarly, Gaglione et al. (2022) examined

accessibility by analyzing population density in

conjunction with urban amenities. By overlaying

population density data onto service area polygons

generated through urban amenity analysis, they

identified the proportion of the population served or

underserved within a 15-minute walking distance.

These reinforce the utility of population distribution

as a key metric for evaluating the "density" dimension

of the 15-minute city, which is the number of

residents who can access available resources,

infrastructure, and spaces (Moreno et al., 2021).

In the Philippines, the Quezon City government is

actively considering the adoption of the "15-minute

city" concept as an important part of its efforts to

foster a livable, environmentally conscious, and

sustainable community for its residents. This

endeavor of the local government unit aims to

stimulate the economy locally through the

decentralization of conventional urban services and

their increased accessibility within local

neighborhoods (Mateo, 2023). This study aims to

develop a geospatial data-based method for

evaluating the "15-minute city" concept in the case of

Quezon City's urban setting.

GISTAM 2025 - 11th International Conference on Geographical Information Systems Theory, Applications and Management

This study aims to create a 15-Minute City Index

(FMCI) that quantifies how closely a region or area

approaches the ideal of 15-minute walking

accessibility to six key SFs: living, working, caring,

supplying, learning, and enjoying. The research

focuses specifically on walking as the mode of

transportation, using a walking speed tailored to

Metro Manila, Philippines, with a case study in

Quezon City. The index calculation, based on the

barangay, the smallest administrative unit in the

Philippines, incorporates barangay-level population

distribution data by age group and the weighted needs

specific to each group.

The FMCI is scored between 0 and 1, where a

score of 0 represents no accessibility to any of the six

SFs, and a score of 1 signifies full access to all six

functions within a 15-minute walking distance,

meeting the age-specific needs of the population.

With this, the paper aims to perform service area

network analysis or isochrone analysis for the six SFs,

to develop a geospatial framework for the 15-minute

city, define the FMCI and model its dependence on

age-group-specific population distribution, calculate

the FMCI for each barangay, the smallest

administrative unit in the Philippines, and to examine

spatial autocorrelation to identify clustering patterns

of FMCI values.

2 METHODOLOGY

2.1 Geospatial Framework

Figure 1 presents the geospatial framework for

developing the 15-minute city index in the selected

case study area of Quezon City. This displays the

interplay and relation of the five key components of

the FMCI, which include age group classification,

pedestrian network, population distribution, the six

SFs, and walking speed, based on literature. This

paper adopts the six SFs defined by Moreno et al.

(2021) to identify urban amenities offering various

services, which will be referred to as 'points of

interest' in this study.

Network analysis, in particular service area

analysis, is employed to assess the proximity of

services with time as the cost (ESRI, n.d.).

Consequently, this required the incorporation of road

network data. To emphasize the pedestrian-centric

principle of the 15-minute city, walking was chosen

as the transportation mode. The selected walking

speed was utilized to generate service areas around

the points of interest.

Figure 1: Geospatial framework for calculating FMCI.

Moreover, while many existing studies utilized

population household locations as starting points, this

research took on a different approach inspired by

Developing a Geospatial Framework for Calculating a 15-Minute City Index (FMCI): The Case of Quezon City

Gaglione et al. (2022), wherein the origins of

proximity analysis were points of interest. As such,

the regions reached within 15 minutes from the points

of interest delineate the population household

locations within the service area. The calculation of

the 15-minute city index depended on the age-based

weight of SFs and population distribution of the age

classification with respect to the six SFs, while the

analysis of results involved spatial statistics. These

will be discussed further in the subsequent sections.

It is important to note that this framework was applied

in the case of Quezon City, Philippines.

2.2 Data Gathering

2.2.1 Age Group Classification

The age group classification in this study is based on

the common age structure used for population

distribution in the Philippines (Philippines Age

Structure - Demographics, 2020), which is 0 - 14

years, 15 - 34 years, 35 - 54 years, 55 - 64 years and

65 - years and above. Given the focus on walking as

the mode of transport for assessing the 15-Minute

City Index (FMCI) in Quezon City, the first age group

has been adjusted to 7–14 years. This modification

aligns with the onset of independent mobility among

children, which typically begins around age seven

(Schoeppe et al., 2015). This adjustment is crucial for

the assessment, as it ensures the evaluation considers

the accessibility of services for children capable of

independent walking. By doing so, the study ensures

that points of interest (POIs) are realistically

reachable within a 15-minute walk for this

demographic.

Moreover, considering the median marrying age

in Quezon City, which is around 29 to 30 years (PSA,

2023), the classification has been further adjusted to

better categorize and address the specific needs of

those below and above these ages. Below is the

revised and final age group classification which will

be used in this study:

• 7- 14 years

• 15 - 29 years

• 30 - 54 years

• 55 - 64 years

• 65 years and above

2.2.2 Road Data and Land Use

Road data for Quezon City was acquired using the

QuickOSM plugin in QGIS, which enables the

retrieval of freely available OpenStreetMap (OSM)

data (QGIS Documentation v: 3.34, n.d.), which is

completely available for the area. To properly

account for facilities outside the study area that may

be within the proximity of areas that are near said

boundaries, a one-kilometer buffer was applied,

allowing the inclusion of routes extending beyond

Quezon City's immediate boundaries. Figure 2

illustrates the raw road data prior to data cleaning and

network establishment.

Figure 2: Quezon City’s Road network is extended by a

one-kilometer buffer.

2.2.3 Population Distribution

The population data utilized in this study originates

from the 2020 Barangay Census of Quezon City,

sourced from the Humanitarian Data Exchange

platform and managed by the Office for the

Coordination of Humanitarian Affairs (OCHA)

Philippines. It includes information on the total

population as well as population breakdowns by age

and sex for each barangay in Quezon City.

2.2.4 Points of Interest

In this paper, POIs from the open dataset are

classified into the six SFs defined by Moreno et al.

(2023). These functions represent key aspects of daily

life and urban needs, with each category

encompassing specific amenities that serve these

roles. This paper adopts this methodology by

focusing on the available facilities related to the set of

terms and concepts specifying each SF, based on

GISTAM 2025 - 11th International Conference on Geographical Information Systems Theory, Applications and Management

Table 1. Table 2 presents the POIs categorized under

each SF and their respective formats and sources.

Table 2: Information on the POIs.

Social

Function

Points of

Interest

Data

Format

Data Source

Living

LRT1 & 2,

MRT3 Train

Stations

Point

Land

Transportation

Office

QC Bus

Stations

Point

Quezon City

Governmen

EDSA

Carousel Bus

Stations

Point Edsa Busway

Barangay Halls

Quezon City

Hall

Point OpenStreetMap

Working

Commercial,

Industrial, &

Institutional

areas

Polygon

Quezon City

Government

Supplying

General shops

Supermarket

Convenience

Stores

Public Market

Point OpenStreetMap

Caring

Hospitals

Pharmacies

Health Centers

Learning

K-12 Public

Schools

Enjoying

Museum

Picnic sites/

Park

Theme park,

Water park

Zoo

Amusement

Arcade

Garden

Playground

Swimming

Areas

Malls

2.2.5 Walking Speed

In a walkability study by Gerilla (1995), a mean

walking speed of 70.62 meters per minute, or

approximately 4.23 kph, was determined for Metro

Manila. This speed was established by estimating

walking distance in the region and has since been

used in other walkability studies in the Philippines

and Asia to reflect the walking speed in Metro Manila

(Mateo-Babiano & Ieda, 2007; Tolentino & Sigua,

2022). This speed was uniformly used to create

service areas from sets of POIs.

2.3 Data Preparation

2.3.1 Getting Points for the Working Social

Function

Figure 3 shows the land use (LU) classification of

Quezon City obtained from the city government.

Based on local regulations, one location can only be

classified into one LU. Based on this, commercial,

industrial, and institutional areas within Quezon City

are designated as the working areas within the city to

simplify the identification of the places of work. This

assumes that people working from home or those with

no fixed places of work (delivery workers, etc.) are

not considered.

Figure 3: Land use classification of Quezon City.

These areas are converted into points of interest

for the working SF by generating points along the

boundary of the polygon features. Apart from the

boundary points serving as entry points to these areas,

centroids were not used to avoid over-generalization

of the POI. The interval of the generated points is

based on the maximum distance reached in 15

minutes using our assumed walking speed based on

Equation 1.

𝑀𝑎𝑥.𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑤𝑎𝑙𝑘𝑖𝑛𝑔 𝑠𝑝𝑒𝑒𝑑 × 𝑡𝑖𝑚𝑒 𝑐𝑜𝑠𝑡

(1)

Developing a Geospatial Framework for Calculating a 15-Minute City Index (FMCI): The Case of Quezon City

where:

walking speed = 70.62



m/min



time cost =15



min



As a result, points along the working area

boundary were generated at 1059.3-meter intervals to

reflect the 15-minute temporal distance between each

adjacent point.

2.3.2 Calculating the Age Group-Based

Population per Barangay

The population for ages 0-6 was subtracted from the

total population, consistent with the adopted age

ranges. Given that the raw population data is

aggregated at ranges finer than what is adapted in this

study, the population for each age group was obtained

by summing the population data within the specified

range. The ratio of each age group relative to the new

total population per barangay was also calculated.

2.3.3 Weighting of Social Functions

Pedestrian characteristics are important in studies

involving walkability (Gorrini et al., 2023).

Therefore, in this study, a set of weights for each SF

was established for each group. To accomplish this,

seven experts in various fields relevant to the study,

including sociology, geography, geomatics, and

urban planning, are consulted to characterize the

population for each age group. The experts were

requested to rank each SF based on its importance for

each age group. Table 3 shows the resulting average

rank for the SFs per age group.

Table 3: Average rank of SFs per age group.

Social

Function

Ranking for each age group

7-14 15-29 30-54 55-64 ≥ 65

Livin

3 2 1 1 2

Working 6 5 2 2 6

5 6 3 5 4

Caring 4 4 3 3 1

Learnin

1 1 6 6 5

Enjoying 2 3 3 4 3

Then, the weight of each rank was calculated through

Equation 2. Table 4 shows the computation of the

weights for each rank based on using values from

Table 3 in this equation.

𝑊



𝑚



𝑘





(2)

where:

𝑊



= weight of SF 𝑖

𝑚=total number of SFs

𝑛=rank of SF 𝑖

Table 4: Calculation of weight per rank.

Rank Calculation Weight





0.4083





0.2417





0.1583





0.1028





0.0611





0.0278

Based on these results, the final weights of each

SF for each age group are established. It is important

to note that from Table 3, experts ranked supplying,

caring, and enjoying SFs equally for the age group

30-54 years old, thus having the same weight. Since

the 3 SFs are tied in ranking, the weights for ranks 3-

5 were added and divided by 3 and assigned to the

three SFs to make sure the sum of weights is still 1.

The results are presented in Table 5.

Table 5: Weights of SFs per age group.

Social

Function

Weights for age group

7-14 15-29 30-54 55-64 ≥ 65

Livin

0.1583 0.2417 0.4083 0.4083 0.2417

Workin

0.0278 0.0611 0.2417 0.2417 0.0278

Suppl

0.0611 0.0278 0.1074 0.0611 0.1028

Carin

0.1028 0.1028 0.1074 0.1583 0.4083

Learnin

0.4083 0.4083 0.0278 0.0278 0.0611

0.2417 0.1583 0.1074 0.1028 0.1583

Sum 1.00 1.00 1.00 1.00 1.00

2.3.4 Preparation of Network Dataset

To generate the pedestrian road data from the raw

road dataset from OSM, features that are accessible to

pedestrians are retained. Hence, the network data

included pedestrian lanes, footbridges, and paths.

Primary and trunk roads were also still utilized, but

only in the areas where there were walkable paths,

such as exterior lanes. Google Maps and Google

Streetview were used to verify walkable paths across

the road network. Roads accessible to vehicles only

are removed, with motorways like expressways,

skyways, and U-turn slots removed from the network.

Road features that were not in the raw OSM network

were added and properly referenced. Extensive

intersections were checked for proper pedestrian

routes. Residential roads were included in the road

network, along with some service roads that access

GISTAM 2025 - 11th International Conference on Geographical Information Systems Theory, Applications and Management

residential areas. Service roads that go into

institutional areas like schools and hospitals were

removed from the network.

2.4 Network Analysis

Service areas (SA) from each of the six sets of POIs

were generated via the Network Analyst tool of

ArcGIS using the 15-minute walking time cost as the

impedance on the finalized pedestrian network

dataset. The direction was set away from the

facilities, which were set to be the POIs.

2.5 Calculating the 15-Minute City

Index

2.5.1 Generation of Regions and Age Group-

Based Scoring

Figure 4: Resulting regions from combining service areas.

Each service area generated from 2.4 is assigned a

score of 1 for the SF it represents and a score of 0 for

the other SFs. In the context of this study, we define

a region as an area covered by a unique set of service

areas. To generate the overlapping regions of the

service area polygons, the six service areas are

combined. Figure 4 visualizes the combination of the

service areas, where the colors of the features in the

larger map show the number of service areas that

cover said location.

This map, however, does not illustrate the regions

themselves. For example, two areas valued “3” on the

map may not be the same region since each may

include different sets of 3 service areas. In contrast,

the colors in the inset map visualize the unique set of

service areas that cover the location. Since each

region combines a set of service areas, it has five

binary scores, each containing 1 (for an SA present in

the region) or 0 (for an SA not in the region). These

binary scores can be transformed into age-based

scores by multiplying them by the scores in Table 5

to obtain age group-based (AGB) scores per region.

2.5.2 Transforming Region Scores to Scores

per Barangay

An area-based overlay operation is used to re-

organize the AGB scores per region into the

barangays. We can compute the percentage of

barangay areas that each region covers by dividing

the region within the barangay's area by the

barangay’s area. The region’s five AGB scores are

multiplied by the percentage of barangay area that the

region covers. Then, the intermediate scores of each

barangay are derived by summing all the

corresponding AGB of the regions belonging to that

barangay. At the end of this step, the barangay has a

set of five intermediate scores corresponding to the

five age groups.

According to Section 2.3.2, each barangay has an

attribute for the population per age group. The

intermediate scores were multiplied by the

corresponding age group percentages. Finally, the 15-

minute city index is obtained from the sum of these

weighted scores.

2.5.3 Clustering Analysis

To further support the FMCI generated per barangay,

its global and local spatial autocorrelation was

assessed through Moran’s I statistics. Similar

parameters were applied for both global and local

spatial autocorrelation. In the analysis, the

Conceptualization of Spatial Relationships was set to

be inverse distance, which imposes a larger influence

on closer features compared to farther features.

Concerning the distance method, Euclidean distance

was applied since, at this point, solely the FMCI are

considered among barangays without regard to roads

and borders, thus only requiring a simple distance

between barangays. Moreover, the standardization

parameter was set as ‘row,’ which is appropriate for

polygon data, and for aggregated data, which applies

to barangays’ FMCIs.

Developing a Geospatial Framework for Calculating a 15-Minute City Index (FMCI): The Case of Quezon City

3 RESULTS AND DISCUSSION

3.1 Service Areas

Six maps representing the coverage area of the six

SFs discussed were made, with a 100-meter tolerance

distance for the network analysis. The generated

maps are in Figure 5. It can be observed that the

location of services is heavily concentrated in the

southern part of the city. Notably, La Mesa Dam, a

watershed area, is in the northern part of Quezon City.

The presence of this watershed reservation explains

the lack of points of interest and, consequently, low

coverage of service areas, as displayed by the large

space in the northern area.

3.2 Scoring Regions and Barangays

As discussed in Section 2.5.1, regions were generated

from overlaying service area datasets, and AGB

scores were obtained by summing corresponding

weights. Intermediate scores were determined

following the methodology detailed in Section 3.5.2.

Higher intermediate scores suggest a closer proximity

to achieving a high 15-minute city index. Similar to

scored regions, the specific needs of different age

groups are considered in this part through the weights

of SF.

The complete results of the intermediate score for

each barangay, categorized by age group, are shown

in Figure 6. Darker areas imply a higher number of

overlapping service area datasets, as AGB scores are

from the summation of weights. However, it is

important to note that this does not necessarily

indicate a higher population served in darker areas, as

the population distribution of each age group has not

yet been taken into consideration. Higher

intermediate scores suggest a closer proximity to

achieving a high 15-minute city index. These scores

illustrate the specific needs of different age groups,

which are considered in this part through the weights

of the SFs.

Figure 5: Generated service areas and points of interest for each of the six SFs. Each dot represents a POI.

GISTAM 2025 - 11th International Conference on Geographical Information Systems Theory, Applications and Management

Figure 6: Age-Group-Based Scores per barangay.

3.3 15-Minute City Index

After summing up the products of each calculated

intermediate score by the corresponding barangay

population percentage for each age group, the 15-

minute city index (FMCI) of each barangay is

generated. Figure 8 shows the distribution of

barangays categorized by FMCI ranges. These ranges

are established through the division of the entire

FMCI spectrum into five equal intervals, from the

minimum to the maximum value.

Out of 142 barangays, 56 achieved a perfect

FMCI of one, representing 40% of the total barangays

in Quezon City. This indicates that residents of all age

groups of these barangays have access to the six SFs

within a 15-minute walk. Conversely, the lowest

FMCI of 0.066 was recorded for Pasong Putik.

Situated in the northern part of Quezon City near the

Sierra Madre Mountain Range and La Mesa Dam, the

low FMCI of Pasong Putik implies that this area has

low accessibility to the services defined by the six

SFs. Table 6 shows the complete list of barangays that

achieved a perfect FMCI and those that recorded the

three lowest FMCIs in Quezon City.

Figure 7: FMCI for each barangay in Quezon City.

Developing a Geospatial Framework for Calculating a 15-Minute City Index (FMCI): The Case of Quezon City

Table 6: Barangays with the highest and three lowest FMCI.

FMCI Barangay Names

(highest)

Alicia, Amihan, Bagumbuhay, Balong

Bato, Bayanihan, Blue Ridge B, Bungad,

Claro, Damayan, Del Monte, Dioquino

Zobel, Duyan-duyan, E. Rodriguez, East

Kamias, Escopa I, Escopa II, Escopa III,

Escopa IV, Kamuning, Katipunan, Krus Na

Ligas, Lourdes, Maharlika, Malaya, Mariblo,

Marilag, Masagana, Masambong, Milagrosa,

Obrero, Paraiso, Pinagkaisahan, Quirino 2-A,

Quirino 2-B, Quirino 2-C, Quirino 3-A,

Ramon Magsaysay, Roxas, Sacred Heart,

Saint Peter, San Antonio, San Martin De

Porres, San Roque, San Vicente, Santa

Teresita, Santo Domingo (Matalahib),

Sienna, Sikatuna Village, Silangan,

Tagumpay, Talayan, Tatalon, Teachers

Village East, Veterans Village, Villa Maria

Clara, West Kamias

0.333

Payatas

0.243

Bagong Silangan

0.066

(lowest)

Pasong Putik Proper

3.4 Global and Local Cluster Analysis

With the FMCI already defined for each

administrative division, this section explores

significant geospatial patterns to identify

relationships concerning the index. The spatial

variation of the FMCI across barangays was

examined using spatial autocorrelation techniques.

As shown in Figure 8, the Global Moran’s I statistic

of 0.485244 indicates positive spatial autocorrelation,

suggesting a clustered distribution of FMCI values

among barangays. The statistical significance of this

result, supported by a z-score of 18.46 and a p-value

near zero, confirms a less than 1% likelihood that the

observed clustering occurred randomly. These

patterns are also visually evident in the FMCI

distribution map in Figure 7.

The positive spatial autocorrelation indicated by

the Global Moran's I suggests that FMCI values are

not randomly distributed across barangays but rather

tend to cluster. This implies that areas with similar

levels of access to SFs are geographically proximate,

reflecting spatial dependencies due to factors like

urban infrastructure, socioeconomic conditions, and

geographic location. The significance of this result

reinforces the reliability of the clustering patterns and

provides a robust foundation for understanding

spatial disparities.

Local Moran’s I provided more granular insights,

identifying barangays as hot spots (High-High or

HH), cold spots (Low-Low or LL), or outliers (High-

Low or HL and Low-High or LH). Figure 9 illustrates

these patterns. Among the 142 barangays, 77 in the

southern part of Quezon City form HH clusters with

high FMCI values, reflecting greater access to the six

SFs. Conversely, 13 northern barangays form LL

clusters. Outliers include 13 LH barangays with open

spaces and 2 HL barangays near the watershed.

Notably, the remaining 37 of 142 barangays are not

statistically significant. Thus, results from the

extensive FMCI hot spots suggest that the 15-minute

city concept is consistently realized throughout most

of the study area.

Figure 8: Global Moran’s I spatial autocorrelation report of

FMCI in Quezon City.

The Local Moran’s I analysis provides a deeper

understanding of the spatial dynamics within Quezon

City. The 77 HH barangays suggest that these areas

benefit from better urban infrastructure, higher

population density, and improved access to essential

SFs. On the other hand, the 13 LL barangays indicate

that natural features such as open spaces and the La

Mesa Watershed limit access to these functions,

creating spatial disparities in service provision. The

outliers suggest that factors like land use (e.g., parks

and cemeteries) and proximity to natural features play

an important role in shaping access to social services

in these areas and highlight the complexity of spatial

patterns and the need for context-specific

interventions to address inequities. The observed

clustering supports the Global Moran’s I results,

emphasizing positive spatial autocorrelation among

barangays. These findings highlight disparities in

accessibility, with southern barangays benefiting

from proximity to Metro Manila's urban core, while

GISTAM 2025 - 11th International Conference on Geographical Information Systems Theory, Applications and Management

northern barangays face limitations due to geographic

and land use constraints.

Figure 9: Cluster/Outlier Map based on Local Moran’s I

spatial autocorrelation of FMCI in Quezon City.

4 CONCLUSIONS

This study explores the 15-minute city concept,

emphasizing the importance of providing residents

with accessible essential services within a 15-minute

radius, thereby enhancing urban quality of life. In

Quezon City, service areas for these functions were

generated using points of interest (POIs), producing

maps that revealed a concentration of accessibility in

the city's southern regions. To contextualize these

findings, a 15-minute city index (FMCI) was

developed, integrating geospatial and demographic

data, including population and age distribution. The

FMCI evaluates how well areas achieve ideal

accessibility to the six SFs.

Analysis showed that 57 of 142 barangays,

predominantly in the south, achieved an FMCI of 1,

signifying access to all six functions. In contrast,

northern barangays like Pasong Putik scored the

lowest (0.066) due to geographical and land use

constraints. Spatial autocorrelation using Moran’s I

confirmed clustering patterns of FMCI values,

identifying significant hot spots in the south and cold

spots in the north.

The FMCI, grounded in a geospatial framework,

provides valuable insights for policymakers to

enhance urban life by addressing proximity,

sustainability, and social equity while aligning with

the 15-minute city concept (Moreno, 2019). To

improve future implementations, service area

generation can be refined by incorporating

walkability in paths and adjusting walking speeds per

age group. Additionally, the 1-km buffer can be

implemented as network buffer, not a Euclidean

buffer. Sensitivity analysis should also be performed

for the expert-given weights to reinforce its

reliability. To overcome boundary effects,

tessellation-based calculation can be performed

instead of calculating the FMCI per barangay, as

calculating FMCI at a finer spatial resolution, such as

the household level, may reveal more nuanced spatial

patterns. Lastly, incorporating service prioritization

within each SF can improve the ranking, tailoring

scores more effectively for different age groups.

ACKNOWLEDGMENTS

The authors would like to thank Gerardo Lanuza,

Rhea Lyn Dealca, Ma. Afrecita Nieva, Aurora Llige,

John Abletis, Jonathan Guevarra, Carlyn Ann Ibañez,

Dominique Sasha Amorsolo, and Kristian Karlo

Saguin for sharing their expertise for evaluating the

SFs for each age group. We would also like to thank

the Department of Science and Technology – Science

Education Institute (DOST-SEI) and the Engineering

Research and Development for Technology (ERDT),

and the Office of the Vice Chancellor for Research

and Development (OVCRD) of the University of the

Philippines Diliman for the faculty research

dissemination grant that supported the publication

and presentation of this paper.

REFERENCES

Akrami, M., Sliwa, M. W., & Rynning, M. K. (2024b).

Walk further and access more! Exploring the 15-minute

city concept in Oslo, Norway. Journal of Urban

Mobility, 5, 100077

https://doi.org/10.1016/j.urbmob.2024.100077

ArchDaily. (2024). In Conversation with Professor Carlos

Moreno, on the 15-Minute City Concept. YouTube.

https://youtu.be/n3u9Oax-

dZs?si=QGHvoD0HaNRWmmxo

Birkenfeld, C., Victoriano-Habit, R., Alousi-Jones, M.,

Soliz, A., & El-Geneidy, A. (2023). Who is living a

local lifestyle? Towards a better understanding of the

15-minute-city and 30-minute-city concepts from a

Developing a Geospatial Framework for Calculating a 15-Minute City Index (FMCI): The Case of Quezon City

behavioural perspective in Montréal, Canada. Journal

of Urban Mobility, 3, 100048.

https://doi.org/10.1016/j.urbmob.2023.100048

Caselli, B. (2021). From urban planning techniques to 15-

minute neighbourhoods. A theoretical framework and

GIS-based analysis of pedestrian accessibility to public

services. European Transport, 85, 1–15.

https://doi.org/10.48295/et.2021.85.10

Esri. (n.d.). Service area analysis layer. Arcgis.Com.

Retrieved June 4, 2024, from

https://pro.arcgis.com/en/pro-

app/latest/help/analysis/networks/service-area-

analysis-layer.htm

Fazio, M., Borghetti, F., Giuffrida, N., Pira, M. L., Longo,

M., Ignaccolo, M., Inturri, G., & Maja, R. (2023). The

“15-minutes station”: a case study to evaluate the

pedestrian accessibility of railway transport in Southern

Italy. Transportation Research Procedia, 69, 536–543.

https://doi.org/10.1016/j.trpro.2023.02.205

Gaglione, F., Gargiulo, C., Zucaro, F., & Cottrill, C. (2022).

Urban accessibility in a 15-minute city: a measure in the

city of Naples, Italy. Transportation Research Procedia,

60, 378–385.

https://doi.org/10.1016/j.trpro.2021.12.049

Gorrini, A., Presicce, D., Messa, F., & Choubassi, R.

(2023). Walkability for children in Bologna: Beyond

the 15-minute city framework. Journal of Urban

Mobility, 3, 100052.

https://doi.org/10.1016/j.urbmob.2023.100052

Gerilla, G. P., Hokao, K., & Takeyama, Y. (1995).

Proposed level of service standards for walkways in

Metro Manila. Journal of the Eastern Asia Society for

Transportation Studies, 1(3), 1041-1060.

Knap, E., Ulak, M. B., Geurs, K. T., Mulders, A., & Van

Der Drift, S. (2023). A composite X-minute city cycling

accessibility metric and its role in assessing spatial and

socioeconomic inequalities – A case study in Utrecht,

the Netherlands. Journal of Urban Mobility, 3, 100043.

https://doi.org/10.1016/j.urbmob.2022.100043

Mateo-Babiano, I., & Ieda, H. (2007). Street Space

Sustainability in Asia: The Role of the Asian Pedestrian

and Street Culture. Journal of the Eastern Asia Society

for Transportation Studies, 7, 1915–1930.

https://doi.org/10.11175/easts.7.1915

Mateo, J. (2023, June 16). Quezon City eyes ‘15-minute

city’ strategy in barangays. Philstar.com.

https://www.philstar.com/nation/2023/06/17/2274466/

quezon-city-eyes-15 minute-city-strategy-barangays

Moreno, C., Allam, Z., Chabaud, D., Gall, C. L., &

Pratlong, F. (2021). Introducing the “15 Minute City”:

Sustainability, resilience and place Identity in future

Post-Pandemic Cities. mart Cities, 4(1), 93–111.

https://doi.org/10.3390/smartcities4010006

Moreno, C., Gall, C., Chabaud, D., Garnier, M., Illian, M.,

& Pratlong, F. (2023, April 11). The 15-minute City

model: An innovative approach to measuring the

quality of life in urban settings 30-minute territory

model in low-density areas. https://hal.science/hal-

04065455/file/TI%20Chair%20White-Paper-

3%20EN.pdf

Papadopoulos, E., Sdoukopoulos, A., & Politis, I. (2023).

Measuring compliance with the 15-minute city concept:

State-of-the-art, major components and further

requirements. Sustainable Cities and Society, 99,

104875. https://doi.org/10.1016/j.scs.2023.104875

Philippines Age structure - Demographics. (2020).

https://www.indexmundi.com/philippines/age_structur

e.html

Philippine Statistics Authority (PSA). (2023, June 29).

Marriage Statistics in Quezon City: 2021. GOVPH.

https://rssoncr.psa.gov.ph/content/NCRII/marriage-

statistics-quezon-city-

2021#:~:text=Marriages%20Registered%20marriages

%20in%20Quezon,figure%20of%20the%20previous%

20year.

Poorthuis, A., & Zook, M. (2023). Moving the 15-minute

city beyond the urban core: The role of accessibility and

public transport in the Netherlands. Journal of

Transport Geography, 110, 103629.

https://doi.org/10.1016/j.jtrangeo.2023.103629

QGIS Documentation v: 3.34. (n.d.).

https://docs.qgis.org/3.34/en/docs/training_manual/qgi

s_plugins/plugin_examples.html

Schoeppe, S., Duncan, M. J., Badland, H. M., Rebar, A. L.,

& Vandelanotte, C. (2015). Too far from home? Adult

attitudes on children’s independent mobility range.

Children’s Geographies, 14(4), 482–489.

https://doi.org/10.1080/14733285.2015.1116685

Shahid, R., Bertazzon, S., Knudtson, M. L., & Ghali, W. A.

(2009). Comparison of distance measures in spatial

analytical modeling for health service planning. BMC

Health Services Research, 9(1).

https://doi.org/10.1186/1472-6963-9-200

Staricco, L. (2022). 15-, 10- or 5-minute city? A focus on

accessibility to services in Turin, Italy. Journal of Urban

Mobility, 2, 100030.

https://doi.org/10.1016/j.urbmob.2022.100030

Tolentino, N. J. Y., & Sigua, R. G. (2022). Characteristics

of Walking and Cycling in Metro Manila, Philippines.

Philippine Transportation Journal, 5(1), 20-40.

https://ncts.upd.edu.ph/tssp/wp-

content/uploads/2022/12/TSSP2022_Vol5-No1_02-

Tolentino-and-Sigua.pdf

Vaughn, W. (2023, October 6). 15-minute cities: Where

they can be found across the globe. City Monitor.

https://citymonitor.ai/environment/15-minute-cities-

where-are-they

Weng, M., Ding, N., Li, J., Jin, X., Xiao, H., He, Z., & Su,

S. (2019). The 15-minute walkable neighborhoods:

Measurement, social inequalities and implications for

building healthy communities in urban China. Journal

of Transport & Health, 13, 259–273.

https://doi.org/10.1016/j.jth.2019.05.005

Zhang, S., Zhen, F., Kong, Y., Lobsang, T., & Zou, S.

(2022). Towards a 15-minute city: A network-based

evaluation framework. Environment and Planning. B,

Urban Analytics and City Science/Environment &

Planning. B, Urban Analytics and City Science, 50(2),

500–514. https://doi.org/10.1177/23998083221118570

GISTAM 2025 - 11th International Conference on Geographical Information Systems Theory, Applications and Management