Despite an extensive network, public transportation in Jakarta struggles with inclusive mobility due to spatial disparities. Rapid urbanization since the 1960s led to economic-driven housing, granting some privileged groups easy access to city center amenities, while leaving others disconnected from economic hubs due to inadequate multimodal transport. To further investigate this issue, this study uses spatial autocorrelation to explore economic clustering based on housing types, followed by network analysis of multimodal urban transport accessibility and isochrone of activity centers using ArcGIS Pro and QGIS. The data used includes public transportation networks and integrated JakLingko programs, such as railbased transportation (KRL, MRT, LRT) and road-based public transportation (TransJakarta, mikrotrans, Royaltrans), followed by the 2022 Spatial Masterplan (RDTR) of DKI Jakarta, administration boundary (RT and RW level), Google Earth Imagery, and published statistics provided by Statistics Indonesia (BPS). Our findings show a correlation between the economic clustering of certain housing blocks and their access to public transportation. Middle to upper-class groups living in Central Jakarta tend to have better access to public transportation than those scattered around Jakarta. We argue that there is a need to reassess Jakarta's existing urban transportation network to develop an inclusive urban transportation system that would allow all city residents living in various residential areas to utilize public transit effectively.
Gerbangkertosusila (Gresik-Bangkalan-Mojokerto-Surabaya-Sidoarjo-Lamongan) is one of the biggest metropolitan areas in Indonesia impacted hardest by COVID-19 after social restriction. High temperature conditions are an issue in the Gerbangkertosusila area. Reduced mobility and industrial activity lead to decrease in surface temperature. The research was carried out using the Statistical Mono Windows (SMW) algorithm in separate periods of time (July 2019, July 2020, October 2020, May 2021) to represent the changes between social restriction policy and the weather. This research goal is to examine the relationship between land surface temperature with changes of spectral indices, such as NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) data. These three parameters are correlated with a simple linear regression equation to calculate how much influence occurs in each different period, then the qualitative analysis is carried out to explain the variations between the distribution of hotspot and annual temperature chart to the real conditions. The result shows strong positive correlation coefficient between changes of NDBI pixel and the LST in each period of time such as 0.62; 0.80; 0.70; and 0.80. Meanwhile the NDVI-LST correlation coefficient shows negative results such as -0.57; -0.43; -0.38; -0.41. This research also concludes that in the social restriction period, the Land Surface Temperature doesn't affect the variability of NDVI
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.