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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Annals</journal-id>
<journal-title-group>
<journal-title>ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Annals</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9050</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprs-annals-X-5-W4-2025-361-2026</article-id>
<title-group>
<article-title>Analyzing Economic Spatial Patterns of the Metro Manila Urban Agglomeration using Nighttime Light Data</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Paliza</surname>
<given-names>Christon Ledge S.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Swin</surname>
<given-names>Krystel Joy S.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Elazegui</surname>
<given-names>Erica Erin E.</given-names>
<ext-link>https://orcid.org/0009-0002-1965-4461</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Claridades</surname>
<given-names>Alexis Richard C.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Department of Geodetic Engineering, University of the Philippines Diliman, Philippines</addr-line>
</aff>
<pub-date pub-type="epub">
<day>10</day>
<month>02</month>
<year>2026</year>
</pub-date>
<volume>X-5/W4-2025</volume>
<fpage>361</fpage>
<lpage>368</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Christon Ledge S. Paliza et al.</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/X-5-W4-2025/361/2026/isprs-annals-X-5-W4-2025-361-2026.html">This article is available from https://isprs-annals.copernicus.org/articles/X-5-W4-2025/361/2026/isprs-annals-X-5-W4-2025-361-2026.html</self-uri>
<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/X-5-W4-2025/361/2026/isprs-annals-X-5-W4-2025-361-2026.pdf">The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/X-5-W4-2025/361/2026/isprs-annals-X-5-W4-2025-361-2026.pdf</self-uri>
<abstract>
<p>The Philippines&amp;rsquo; largest urban agglomeration, Metro Manila, exhibits a concentration of economic activity in densely integrated regions. Although central cities benefit from optimal accessibility and agglomeration economies, this is at the expense of peripheral cities, causing regional economic disparities. This study examined Metro Manila&amp;rsquo;s spatial economic patterns from 2015 to 2020. To capture economic heterogeneity, this study used nighttime light point data from 2015 to 2020 as a proxy for economic development, capturing granular patterns across the region. Spatial network weights were employed to define the neighborhood of the features, capturing economic interactions beyond geographical proximity. Global Moran&amp;rsquo;s I indicated significant spatial clustering, with a gradual decline, suggesting signs of regional convergence. Cluster-outlier analysis revealed persistent core-periphery patterns, with central cities like Manila, Makati, and Quezon City exhibiting concentrated development, while peripheral cities like Valenzuela and North Caloocan lag due to congestion, poor infrastructure, and inadequate business facilities. Notably, Alabang in the Metro South emerged as an economic hotspot, outside the traditional urban cores. High-low outliers detected in Valenzuela highlight uneven intra-city development, while the detection of Forbes Park as a cold spot demonstrates nighttime light&amp;rsquo;s limitation in measuring economic development. Directional analysis identified Mandaluyong as the economic center, reflecting its rapid growth during the period. The analysis showed economic activity following a North-South orientation, aligning with the region&amp;rsquo;s geographical structure, with gradual expansion over time, supporting economic diffusion. Overall, the findings highlight the capability of nighttime light in formulating targeted policies for balanced regional development.&amp;nbsp;</p>
</abstract>
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