Capturing Rural–Urban Region (RUR) Assessment through Integrated Conceptual and Geo–Big Data Approaches: A Case Study from Penajam Paser Utara, Neighboring Indonesia’s New Capital (IKN)

Abstract

The increasingly dynamic development of rural-urban regions (RUR) necessitates the advancement of classification methods for RUR areas. Traditional boundaries, primarily based on land use patterns and population distribution, are no longer sufficient to capture the complex socio-economic dynamics and spatial integration that characterise contemporary RUR landscapes. Penajam Paser Utara (PPU), which directly borders Indonesia’s Nusantara Capital City (IKN), exhibits a high degree of regional dynamism, making it an exemplary case for the application of typological assessment approaches. This research aims not only to redefine RUR by integrating the most recent theories and advancements in data acquisition technologies but also to identify the typology of RUR in PPU Regency. The spatial method of Kernel Density was applied to evaluate critical variables, including activity accumulation, land-use characteristics, connectivity, government services, and population size and density using integrated field data and geospatial big data (e.g., satellite imagery, mobile data). Despite evolving RUR assessment methods, unresolved debates persist regarding the selection of optimal indicators, particularly the role of connectivity in delineating urban boundaries. The findings not only successfully provide new evidence for the application of advanced kernel density models that incorporate multiple data sources to represent key land use and connectivity features, but they also reveal that the study area was mostly rural, with limited urban areas. Penajam and Babulu had larger peri-urban areas, suggesting future urbanization potential. Waru and Sepaku lacked urban areas, confirming their rural nature. These results underline the critical importance of RUR delineation for spatial analysis in PPU and highlight the need for further research on sustainable urban-rural linkages to promote balanced development. This research contributes novel insights into the assessment and classification of RURs, particularly in high-dynamic regions.

Introduction

Urban, peri-urban, and rural are classifications of Rural-Urban Regions (RUR), that represent the stages of regional development, spanning from the city centre to the suburbs and rural areas (Pratomo, Samsura, et al., 2025; Ravetz, Fertner, et al., 2013). Areas characterised by dense and organised structures constitute the first typology of RUR, while peri-urban areas form the second typology, reflecting transitional zones that combine urban and rural elements. Meanwhile, the third typology describes rural regions that are predominantly agrarian and natural in character (Ravetz, Fertner, et al., 2013). Identifying RUR typologies is essential for understanding and managing spatial dynamics that influence human activities (Boiko, 2019), environmental sustainability (Printz and Jung, 2023), and socio-economic development.

However, a key challenge in identifying RUR typologies lies in the increasingly dynamic nature of regional development. This is evidenced by numerous studies that highlight the growing integration between urban and rural areas through processes such as urbanisation, land-use changes, and socio-economic transformations (Qin, Yu, et al., 2023; C. Shi, Zu, et al., 2022). These dynamics are reflected in various case studies, where rural land-use patterns begin to exhibit urban characteristics, albeit with a temporal lag (Qin, Yu, et al., 2023). Furthermore, the rising interdependence between urban and rural areas—facilitated by resource flows, population mobility, and livelihood diversification—has increasingly blurred traditional boundaries (Ørtenblad, Birch-Thomsen, et al., 2019; Primdahl, Andersen, et al., 2013). This highlights the need for a more comprehensive assessment framework that moves beyond traditional considerations, which remain a subject of ongoing debate.

The classification of RUR before 1960’s was initially relied on land use patterns (Bourne, 1974; Krehl and Siedentop, 2019). The assessment of RUR typology primarily through land use methods is considered less relevant because regional development has become more complex and dynamic, which may affect the characteristics (Ravetz, Fertner, et al., 2013). Currently, the swift demarcation between the regions no longer reflect the dynamics of social, economic, and infrastructural development accurately, which requires a more comprehensive method (Sun, Liu, et al., 2023). Therefore, novel theories to determine RUR typology need to use more indicators, including connectivity and activity accumulation (Abrantes, Rocha, et al., 2019; Feng, Peng, et al., 2020; Krehl and Siedentop, 2019; Mortoja and Yigitcanlar, 2023; Sutton, 2003; Zhang and Seto, 2013). A previous study on urban Wuhan showed that using a combination of land use and activity generation data significantly enhanced the accuracy and objectivity in recognizing urban-rural fringe regions (Jing et al, 2023).

Penajam Paser Utara (PPU) Regency directly borders Nusantara Capital City (IKN). Due to the proximity and strong connection to the core area of the IKN, PPU Regency has significant potential to experience development inequality originating from the establishment of IKN as a new city. Reflecting on Brazil, one of the countries that has relocated its capital, the move has shown both positive and negative impacts on surrounding regions, especially in terms of city structure, regional dynamics, and various societal and environmental aspects (Hackbarth and de Vries, 2021; Pratomo, Samsura, et al., 2022; Quistorff, 2015; Rawat, 2005). Penajam Paser Utara (PPU) Regency shares a direct border with Nusantara Capital City (IKN). Due to its proximity and strong connection to the core area of IKN, PPU Regency holds significant potential to experience developmental disparities stemming from the establishment of IKN as a new city. Drawing parallels with Brazil, a country that has relocated its capital, such a move has demonstrated both positive and negative impacts on surrounding regions, particularly in terms of urban structure, regional dynamics, and various socio-environmental aspects (Hackbarth and de Vries, 2021; Quistorff, 2015; Rawat, 2005).

One of the primary challenges of relocating IKN is the massive influx of urbanisation. After IKN becomes operational as the capital city, it is expected to attract a significant population, starting from nearby areas and gradually extending to other regions in Kalimantan (Sutanto, 2022). In 2021, the population of PPU Regency exceeded 185,000, with an increase of over 9,000 residents—6,000 in 2020 and 3,695 in 2021 (Sutanto, 2022). This population is projected to grow to approximately 1.9 million by 2045. Additionally, an ecological burden assessment of IKN estimates that the population within IKN itself may rise to as many as two million people. Considering the dynamic developments occurring in the areas surrounding the IKN, including PPU, it is crucial to evaluate the current RUR conditions at the early stages of IKN construction by employing a more advanced and contemporary approach capable of accommodating the high level of spatial dynamics. Identifying the RUR typology can help mitigate urban sprawl, leading to various regional issues. Moreover, areas initially characterised as rural gradually transition into urban or a combination of the two (Bossuet, 2006; Elhadary, Samat, et al., 2013; Sadiki and Ramutsindela, 2002).

Based on the elaborated explanations above, studies on RUR studies are not a novel endeavour, having been explored as early as the 19th century (Pahl, 1966). Evidence suggests that the distinction between urban and rural regions can be traced back centuries (Weaver, 1995). Over time, research in this area has evolved, particularly in the assessment of RUR typologies. Early approaches to RUR classification primarily relied on population metrics (Bourne, 1974; Cattivelli, 2021) and land-use patterns (Bourne, 1974; Krehl and Siedentop, 2019). However, subsequent theoretical advancements introduced activity generation and connectivity as critical factors in defining RUR typologies (Abrantes, Rocha, et al., 2019; Feng, Peng, et al., 2020; Krehl and Siedentop, 2019; Mortoja and Yigitcanlar, 2023; Sutton, 2003; Zhang and Seto, 2013).

More recently, a study by Sun, Liu, et al. (2023) has reignited debate regarding the indicators used in RUR classification, particularly connectivity (Sun, Liu, et al., 2023), which remains a pivotal element in delineating urban regions. While numerous studies have successfully classified RUR using diverse considerations, there is a pressing need for a comprehensive framework and methodology capable of integrating these varied dimensions. Such an approach must not only provide a holistic basis for RUR classification but also adapt to the dynamic transformations inherent to RUR.

This study seeks to extend the work of Sun, Liu, et al. (2023) and Gajić, Krunić, et al. (2018), both of which emphasise the necessity of an advanced RUR assessment approach that accommodates more dynamic variables, such as connectivity. Additionally, it incorporates insights from previous studies (Y. Yang, Ma, et al., 2017; Z. Yang, Shen, et al., 2021; Yu, Meng, et al., 2023), which stress the importance of refining traditional RUR classification methodologies. This study aims to redefine the methodology for assessing RUR by integrating recent conceptual advancements, contextual considerations, and state-of-the-art data acquisition technologies, including the utilisation of geospatial and big data. This integration seeks to enable a more comprehensive analysis that aligns with the increasingly dynamic nature of RUR.

The proposed framework is applied to Penajam Paser Utara (PPU) Regency as a critical case study, a region experiencing exceptionally rapid transformation due to its pivotal role in the development of Indonesia’s new capital city, Nusantara (IKN). This highly dynamic context positions PPU as an ideal testbed for evaluating the redefined RUR typology framework under conditions of intense urbanisation pressure.

Literature Review

Re-thinking Rural-Urban-Region: Urban, Peri-Urban, and Rural Regions

Urban regions are typically characterised by high population density as well as intense and diverse activity concentrations, driven by supporting infrastructure such as residential, commercial, and industrial zones. Various theories, both past and present, define urban regions through aspects like population density, economic and social activities, and complex infrastructure (Quigley, 1998; Seto, Güneralp, et al., 2012). The regions influence transcending functional and administrative boundaries, as well as act as centres of activity affecting both surrounding and wider areas. Initially, classifications relied on rigid administrative boundaries, but since 2000, urban classification has become more dynamic, transcending the city's administrative limits to offer a more accurate representation of urbanised regions (Ottensmann, 2019).

Peri-urban regions that are situated in areas with urban and rural characteristics (Pratomo, Samsura, et al., 2025; Yunus, 2008) have experienced significant definition changes over time. Until 1999, the term "peri-urban" was regarded as regions on the outskirts of an urban centre that combined urban and rural elements (Eames and Schwab, 1964; Pack, 1917; Tisdale, 1942; Wirth, 1938). Before 2000, peri-urban was defined by its position between urban and rural regions, and the concept became more specific subsequently. In fact, some theories defined peri-urban as regions with various land uses, such as settlement, agriculture, and recreational spaces (Busck, Kristensen, et al., 2006; Trefon, 2009).

Theories on the peri-urban explain its dynamism, multifunctionality, and unique identity. Some of the theories incorporate varying dimensions including socio-economic factors, landscape, and ecological considerations, rather than focusing mainly on proximity to the urban centre (Bogaert, Biloso, et al., 2015; Buxton, 2022; Castles, 2014; Gough, 2020; Tokas, 2021). This change indicates that peri-urban is a dynamic zone with variable boundaries (Sahana, Ravetz, et al., 2023). In other words, the zone has evolved into a distinct region characterised by a mix of built and open spaces that are dispersed, non-contiguous, and surrounding the urban core.

The "rural" definition has evolved over time, with previous studies defining rural regions by economic and physical characteristics, including a focus on agricultural production, low population density, as well as limited infrastructure (Castle, 1998; Johnson and Scott, 1997). Meanwhile, several theories on rural have become more specific, especially regarding the stigma that is related to associated agricultural activities. Furthermore, economic diversification in the regions has become a trend, with many currently engaging in non-agricultural activities. The rural economy in Europe comes from the non-agricultural sector, with over 50% of farmers being involved in economic activities outside of agriculture (Mihai, 2011). This reflects a fading agricultural stigma as rural economies and societies continue to evolve.

According to the theories, the definitions of urban, peri-urban, and rural regions have significantly developed. Urban is characterised by activity concentrations and high population density, supported by infrastructure like residential, commercial, and industrial zones. Changes in the definition of urban around the year 2000 led to the use of smaller geographical units for more accurate representation. Meanwhile, peri-urban, located between urban and rural, has evolved from mere transition zones into areas with multifunctionality and unique identities, and it includes more dynamic boundaries. Currently, many of these regions are involved in non-agricultural activities, which confirmed a change from the agricultural stigma previously experienced. Rural regions have also experienced changes with less emphasis on traditional agricultural characteristics and well as more focus on economic diversification.

Evolution of Rural-Urban-Regions Assessment

Conventional approaches used to identify urban and rural regions depended on a simple dichotomy, which has been shown to be ineffective in addressing the complex and dynamic nature of peri-urban. Currently, there is a shift toward effective methods that integrate demographic, economic, and social indicators to better comprehend the transitional nature of peri-urban (Cattivelli, 2021). Geographic Information Systems (GIS) and remote sensing techniques have also been widely used to analyse land use change as well as identify urban, peri-urban, and rural regions. These methods help to assess spatial patterns and dynamics, as well as building density and other indicators, which contribute to an in-depth understanding of the regions (Sasongko, Gai, et al., 2024; Wolff, Mdemu, et al., 2021). The shift in the identification of RUR typology is presented in Figure 1.

Figure 1. Evolution of Rural-Urban-Regions Typology Assessment

Source: Elaborated from Chambers, 1931; Dong, Qu, et al., 2022; Gottero, Cassatella, et al., 2021; W. Huang, 2016; J. Li, Xie, et al., 2021; Myers and Beegle, 1947; Saksena, Fox, et al., 2014; Sun, Liu, et al., 2023; Sutton, 2003; Whitehand, 1967; Wu, Wang, et al., 2021; Xu, Ma, et al., 2018; Z. Yang, Shen, et al., 2021; Zheng, Seto, et al., 2023

Prior to 2009, studies on land use primarily focused on the division between built-up and unbuilt land, as well as agricultural land distribution (Pryor, 1969; Uttarwar and Sokhi, 1989). Meanwhile, most recent studies emphasised the scope of identifying urban and rural regions, transcending agricultural land use as the sole indicator of rural (Danielaini, Maheshwari, et al., 2018; I. Dutta & Das, 2019; Goerlich Gisbert, Cantarino Martí, et al., 2017; Gonçalves, Gomes, et al., 2017; Mondal and Banerjee, 2021; Saksena, Fox, et al., 2014; Wolff, Mdemu, et al., 2021; Z. Yang, Shen, et al., 2021; Zlender, 2021). After 2009, several studies started using activity generation variables, including nighttime satellite imagery to identify urban, peri-urban, and rural regions (Feng, Peng, et al., 2020; Sutton, 2003; Sutton, Goetz, et al., 2010; Zhang and Seto, 2013; Zheng, Seto, et al., 2023). The development of satellite technology, especially high-resolution sensors, has significantly improved the ability to map of RUR typology. Moreover, connectivity is another important consideration in the evolution of RUR classification methods. Before 2009, commuting patterns were used to identify RUR typology (Morrill, Cromartie, et al., 1999). Similarly, Vasanen (2012) defined it through origin-destination flow, showing how each centre is functionally connected to other parts of the urban system. Furthermore, connectivity has been explored through road density to urban transportation networks (Abrantes, Pimentel, et al., 2010; Sun, Liu, et al., 2023; Wolff, Mdemu, et al., 2021). Studies have emphasised that connectivity influences urban growth, transportation networks, ecological interactions, and socio-economic dynamics, making it an essential factor in the classification of urban and peri-urban (Arif and Gupta, 2020; L. Li, Tang, et al., 2023; Nath and Kumar, 2020; Winarso, Hudalah, et al., 2015).

Connectivity, in a broader sense, extends beyond mere commuting patterns, offering a more profound understanding of the intricate interactions within urban environments. A particularly significant aspect in this regard is the inclusion of Points of Interest (POI), especially those related to government services (Tobing, Situmorang, et al., 2023), which provide valuable insights into the characteristics of urban regions (Z. Yang, Shen, et al., 2021). Previous studies have explored how government services—including administrative institutions, public utilities, catering, shopping, education, entertainment, life services, automotive services, hotels, residential areas, and financial institutions—serve as indicators for identifying urban characteristics (Dong, Qu, et al., 2022; J. Li, Xie, et al., 2021; Sun, Liu, et al., 2023; Wu, Wang, et al., 2021).

The classification of RUR using government services incorporates spatial distribution as a critical factor in delineating RUR typologies, with these services acting as proxies for human activity and urban functionality. This approach has proven instrumental in capturing the complex dynamics of RUR typologies(Gottero, Cassatella, et al., 2021; W. Huang, 2016; Sahoo and Sreeja P., 2012; Sun, Liu, et al., 2023; Xu, Ma, et al., 2018). Furthermore, population size and density have historically served as foundational metrics for identifying urban and rural characteristics (Abrantes, Pimentel, et al., 2010; Banzhaf, Grescho, et al., 2009; Cattivelli, 2021; Danielaini, Maheshwari, et al., 2018; I. Dutta & Das, 2019; S. Dutta, Sharma, et al., 2022; Goerlich Gisbert, Cantarino Martí, et al., 2017; Mondal and Banerjee, 2021; Myers and Beegle, 1947; Pryor, 1969; Vasanen, 2012). While these metrics remain relevant in recent studies, their interpretation has evolved to align with modern urban dynamics.

However, the variables discussed above have largely been applied in partial rather than as part of an integrated framework for RUR assessment. This fragmented approach is insufficient for regions experiencing high levels of dynamism, where a comprehensive and holistic evaluation of RUR is imperative (Beynon, Crawley, et al., 2016).

Rural-Urban-Regions Dynamic Assessment Theoretical Framework

The definitions of urban, peri-urban, and rural (RUR) areas have evolved significantly, necessitating a shift in the considerations used to classify RUR typologies. Initially reliant on land-use characteristics in the 1960s, by 2009 these considerations expanded to encompass five key factors: population size and density, activity accumulation, land-use characteristics, connectivity, and government services. This evolution reflects the increasingly dynamic nature of RUR (Delgado-Viñas and Gómez-Moreno, 2022; Olson and Munroe, 2012), yet it has also introduced challenges in accurately assessing these areas, resulting in a continuum, hybrid, or liminal state where boundaries between categories become ambiguous (Rusta, 2018). Despite the enhanced complexity and methodological diversity of post-2009 frameworks, their partial application has created uncertainties regarding the most suitable considerations for capturing the highly dynamic nature of RUR.

This research consolidates existing considerations and proposes a theoretical framework to integrate all five factors, enabling a more comprehensive assessment of RUR typologies. The framework, illustrated in Figure 2, aims to address the fluid and dynamic characteristics of RUR areas. Among these considerations, activity accumulation emerges as a context-independent indicator of human activity, represented through nighttime light data, which effectively captures activity intensity across regions (Zheng, Seto, et al., 2023). Urban areas, with higher population densities and advanced infrastructure, exhibit greater frequencies of activity compared to rural areas, as evidenced by higher levels of physical activity and better access to facilities in urban environments (Martin, Kirkner, et al., 2005; Whitfield, Carlson, et al., 2019).

Figure 2. Theoretical Framework of RUR Typology Assessment

The second consideration, government services, plays a crucial role in shaping the dynamic characteristics of urban and rural areas by influencing infrastructure distribution and spatial patterns of activity (Y. Zhou, 2023). Recent studies demonstrate that integrating government services data with nighttime light significantly enhances the accuracy of RUR typology identification—from 84.32% to 95.02%—underscoring its importance as a key indicator in distinguishing and mapping RUR dynamics (Y. Chen and Deng, 2022).

Population size and density remain fundamental to understanding urban and rural dynamics, reflecting spatial distribution patterns and demographic trends (Dwaraka Srihith, Aditya Sai Srinivas, et al., 2023). Urban areas, characterised by high population density and concentrated infrastructure, serve as economic hubs but also face challenges such as congestion and pollution (Craig and Haskey, 1978). In contrast, rural areas, with lower population densities, often function as sources of essential resources for urban centres. This study advances the application of this consideration by employing residential building density as a more precise, boundary-independent measure of population size and density (Le Tourneau, 2018).

Connectivity, a long-established consideration since the 1960s, facilitates interactions, economic activities, and resource accessibility across regions. However, its representation varies widely, encompassing commuting patterns, road density, and urban transportation systems (Abrantes, Pimentel, et al., 2010; Morrill, Cromartie, et al., 1999; Sun, Liu, et al., 2023; Wolff, Mdemu, et al., 2021). This study introduces a novel perspective by employing network centrality, such as degree centrality, to analyse urban road networks. This approach identifies central routes and subareas, offering insights into spatial cognition and collective behaviour, which are critical for urban planning and development (Porta, Crucitti, et al., 2006).

Finally, land-use characteristics remain the longest-standing consideration, predating the 1960s. Land-use patterns are indispensable for understanding RUR dynamics, reflecting the complex interplay between human activities and natural landscapes, as well as the impacts of urbanisation, economic development, and policy changes (Sunil and Bhaduri, 2024). Case studies in regions such as suburban Delhi, Jiangsu (China), and Shandong Province highlight how shifts from agricultural to residential or industrial land use serve as key indicators of RUR transformation and dynamic transition zones (Z. Yang, Shen, et al., 2021; T. Zhou, Kennedy, et al., 2020). As such, land-use characteristics are not only vital but also essential for capturing the differentiation and transformation within RUR areas.

By integrating these five considerations into a cohesive framework, this study provides a comprehensive approach to assessing RUR typologies, addressing the challenges posed by their increasingly dynamic and complex nature.

Data and Methods

Study Area

This investigation focused on Penajam Paser Utara (PPU) Regency as a case study. In this context, PPU is among the regencies directly adjacent to the Nusantara Capital City (IKN), and it comprised of four sub-districts, which include Penajam, Sepaku, Waru, and Babulu (map in Figure 3). Parts of PPU were transferred to become IKN region in 2022 through Law Number 3 of 2022. Therefore, this relocation was expected to affect the urban structure, economic activities, infrastructure, as well as population distribution in the surrounding regions (Ishenda and Guoqing, 2019).

Figure 3. The Map of Penajam Paser Utara

PPU Regency is strategically positioned along the East Kalimantan coast, sharing borders with Balikpapan and Kutai Kartanegara. The region is predominantly characterised by undeveloped land, presenting substantial opportunities for urban expansion and infrastructure development. Its key economic sectors include agriculture, fisheries and forestry, mining and quarrying, as well as processing industries (Mesoino, Naukoko, et al., 2022). With ongoing infrastructure projects and the planned development of Indonesia's new capital (IKN), PPU Regency holds significant potential to emerge as a new economic hub, particularly as improvements in infrastructure and connectivity.

Given its direct connection to the IKN project, examining RUR development in PPU is crucial. The regency demonstrates high levels of dynamism due to its role in the construction of Indonesia’s new capital. However, this study does not attempt to compare RUR typologies before and after IKN’s development, as the project is still in its early stages. Future research could address this limitation by conducting longitudinal studies to assess the impacts of IKN’s infrastructure and urbanisation once these developments become more evident.

Data Collection

This study redefined the RUR typology assessment method by integrating previous theoretical and empirical studies. In general, several factors for identifying RUR typology include activity accumulation, population size and density, connectivity, land use characteristics, and government services. Activity accumulation was measured using Nighttime Light (NTL), which helped identify urban centres by analysing the intensity of light emissions, correlating with human activity and infrastructure (Z. Chen, Yu, et al., 2017). Therefore, brighter regions generally reflect higher levels of activity and urbanisation (Feng, Peng, et al., 2020; Sutton, 2003; Sutton, Goetz, et al., 2010; Zhang and Seto, 2013; Zheng, Seto, et al., 2023).

The process of population size and density analysis was carried out through the number of residents in a specific region (Banzhaf, Grescho, et al., 2009; Cattivelli, 2021; Danielaini, Maheshwari, et al., 2018; I. Dutta & Das, 2019; S. Dutta, Sharma, et al., 2022; Goerlich Gisbert, Cantarino Martí, et al., 2017; Mondal and Banerjee, 2021; Myers and Beegle, 1947; Pryor, 1969; Vasanen, 2012). In this study, urban regions were typically characterised by high population density, which was because of the concentration of economic activities (Davila and Allen, 2002). Urban and rural settlements data were also used in the assessment of population size and density. This was because urban settlements tended to have higher densities due to the concentration of industries, trade, as well as services, attracting more residents (Barrios, Bertinelli, et al., 2006; Patel, Angiuli, et al., 2015; Tao Yang and Zhou, 1999).

Government services was another consideration, a key factor in identifying RUR typology. According to World Vision (2017), urban regions are typically characterised by administrative structures such as government offices and courts, as well as concentrations of services like hospitals and education. This study also included various government services considered vital to communities, divided into four types, namely government offices (Agustin and Hariyoko, 2022), trade and services (Molenaar and Floor, 1992), education (Latha and Shanmugam, 2014; Soliman R., Eweida, et al., 2017; Tobing, Situmorang, et al., 2023), and health (Latha and Shanmugam, 2014; Levin and Tadelis, 2010; Soliman R., Eweida, et al., 2017; Tobing, Situmorang, et al., 2023).

The next consideration was land use. Traditional studies on RUR typology identification defined land use by dividing it into built-up and unbuilt land (Pryor, 1969). However, subsequent studies expanded the definition by classifying land into several categories, namely agricultural, recreational, and residential land to better identify RUR typology (Danielaini, Maheshwari, et al., 2018; I. Dutta & Das, 2019; Gonçalves, Gomes, et al., 2017; Mondal and Banerjee, 2021; Saksena, Fox, et al., 2014; Wolff, Mdemu, et al., 2021; Z. Yang, Shen, et al., 2021; Zlender, 2021). In urban regions, land use was often characterised by high-density residential and commercial areas, leading to mixed-use land types (Fang, Yuan, et al., 2018; Pouyat, Pataki et al., 2007). Therefore, study adopted the land use classification from Peng, Zhao, et al. (2016), categorising land into four types, namely unused land, forest/water body/shrubs, cultivated land/plantations, as well as transportation/settlement/industry.

The final consideration, connectivity, has been explored in previous studies through various approaches, such as commuting patterns (Vasanen, 2012), road density, and urban transportation systems (Abrantes, Pimentel, et al., 2010; Sun, Liu, et al., 2023; Wolff, Mdemu, et al., 2021). This study, however, redefines connectivity using the concept of network centrality, which identifies the most significant nodes within a network based on their connectivity (Dwyer, Hong, et al., 2006; Skibski, Rahwan, et al., 2019). Network centrality is a critical tool in network analysis, measuring the influence of nodes within a system and closely aligning with connectivity, as its metrics often rely on the structural properties of the network to assess a node's importance.

Specifically, this study employs degree centrality, which calculates the number of direct connections a node has. This measure has been widely used to identify highly connected nodes within networks (Derrible, 2012). Degree centrality is often described as a straightforward connectivity metric, computed by counting a node's direct connections. Its simplicity makes it particularly advantageous for analysing large networks due to its low computational complexity (Zhao, Guo, et al., 2017). While degree centrality may appear basic, its integration with government services enhances its ability to capture regional connectivity more effectively (Lee and Seo, 2023). This refined depiction of connectivity distinguishes this study from previous research.

Moreover, this study emphasises the use of comprehensive variables, offering a more integrated approach compared to earlier studies that applied these five considerations separately. The data summary supporting this analysis is presented in Table 1.

Table 1. Data Collection Methods

Variable	Sub-Variable	Code	Data	Sources
Activity Accumulation	Nighttime Light	NTL	Nighttime Light Index	https://www.sdgsat.ac.cn/
Population Size and Density	Settlement Type	ST	Urban Settlement	Spatial Planning Agency, 2023
			Rural Settlement
Connectivity	Network Centrality	NC	Road Network	https://www.openstreetmap.org/
Land Use Characteristic	Land Use Type	LUT	Unused Land	Spatial Planning Agency, 2023
			Forest, Water Body, Grassland
			Cultivated Land, Garden Plot
			Transportation Land, Residential area, and Industrial Land
Government Services	Government Services	GS	Government Offices	Google API
			Trade and Services
			Education
			Health

Analysis Method

In pursuit of the research objectives, a structured sequence of activities—comprising the input, processing, and output phases—was undertaken. A comprehensive illustration of these stages is presented in Figure 4.

Figure 4. Analysis Method

Based on Figure 4, each consideration has a specific processing technique to obtain its value at a given point/region. For instance, to calculate connectivity through network centrality, analysis is conducted using the v.net centrality plugin to determine the degree of centrality. For population size and density as well as land-use characteristics, these are represented through urban and rural settlements and various land-use categories, such as unused land, forest, water bodies, grasslands, cultivated land, garden plots, transportation land, residential areas, and industrial land. To process these variables, polygon data is first converted into raster formats. The raster files are subsequently sampled to extract values corresponding to specific locations or regions.

Similarly, for activity accumulation, nighttime light (NTL) emission data is utilised. These NTL emissions are also processed into raster formats, and values are sampled from the rasters for the target regions or points. Once the values for each variable are obtained, a scoring process is applied. Each point's value across the five variables is assigned a score based on the scoring guide provided in Table 2. This systematic scoring ensures consistency and comparability across all considerations.

Table 2. Scoring

Variables	Score
	1	2	3	4
NTL	1-87	88-411	412-1.456	1.457-4.095
ST	Rural Settlement	-	-	Urban Settlement
NC	0.00008-0.000175	0.000176-0.000262	0.000263-0.000350	0.000351-0.000527
LUT	Unused Land	Forest, Water Body, Grassland	Cultivated Land, Garden Plot	Transportation Land, Residential area, and Industrial Land
GS	Trade and Service Facilities	Educational Facilities	Health Facilities	Government Offices

Source: Modified from J. Li, Xie, et al., 2021; Peng, Zhao, et al., 2016; Sun, Liu, et al., 2023

Peng, Zhao, et al. (2016), in their study to identify the urban-rural fringe, utilised four types of land use classes categorised based on land use intensity or building density. This approach aligns with studies indicating that urban areas are generally associated with high land use intensity (Y. Shi, Zheng, et al., 2024). This classification method is adopted in this study without modification, as the land use typology proposed by Peng, Zhao, et al. (2016) remains general and relevant to the characteristics of the study area.

Meanwhile, the parameters adopted from Li, Xie, et al. (2021) include the classification method for government services used to delineate urban functional areas. In their study, scoring of government services was based on the dominance of each service over others, reflecting its functional significance in urban dynamics. However, in this study, modifications were made to the scoring of Government Services (GS) and Service Trade (ST) to better reflect local spatial characteristics. Unlike the urban context in Li, Xie, et al. (2021), where economic growth centres are typically concentrated in the city centre, the study area exhibits a different pattern where government and public facilities form the primary activity nodes. Therefore, the scoring scheme for GS and ST was adjusted to accommodate this local context.

Furthermore, Sun, Liu, et al. (2023) employed Nighttime Light (NTL) data to map peri-urban areas by considering light intensity values as indicators of urbanisation. However, this study applies a different scoring approach. Specifically, scoring for NTL data was based on the method proposed by Sutton, Goetz, et al. (2010), which classifies urban and peri-urban areas based on nighttime light intensity, while accounting for the highest and lowest raster values as well as existing local conditions. Compared to previous reference studies (e.g., Sun, Liu, et al., 2023), the absolute values of NTL in the Penajam Paser Utara (PPU) area are relatively lower, as the region is still under development. To ensure proportionality in the scoring, data normalisation was conducted prior to analysis.

In the subsequent stage, each scored variable was subjected to kernel density analysis to identify regions not captured by each variable. Kernel Density Estimation (KDE) is a statistical method used to estimate the probability density function of a random variable. This process involves applying a kernel function to each data point and summing the kernels to create a smooth density estimate. The choice of the function, such as the normal density, affects the shape of the density estimate. By adjusting the smoothing based on local data density, KDE provides a flexible and effective way to visualise and analyse data distributions in both univariate and multivariate contexts (Silverman, 2018). KDE performs better than other methods, especially when there is heterogeneity in cluster size and density (Vestal, Carlson, et al., 2021). It is calculated using the following equation.

$Searchradiusfordensity:0,9xmin(SD,(1ln\left(2\right)Dm)xn-0$ 1

Where:

SD : Standard Deviation

Dm : Median Data

n : Number of facilities

The kernel density analysis results are visualised as a spatial heatmap, which provides a composite representation of density values across different regions or locations. A spatial heatmap employs color gradients to effectively illustrate the intensity or concentration of data points, allowing for a clear depiction of spatial distribution patterns. This visualisation technique is particularly valuable in studies of Rural-Urban-Region (RUR) typologies, as it facilitates intuitive interpretation of spatial dynamics and relationships within urban and peri-urban environments. By enhancing the ability to identify patterns and distributions, spatial heatmaps contribute to more precise and informed analyses in urban studies.

The development of typology maps using the histogram method was employed to classify urban, peri-urban, and rural areas in Penajam Paser Utara. The histogram method is a crucial technique in image processing and data analysis, providing a graphical representation of the frequency distribution of pixel intensity values in an image (Figure 5). This approach is widely utilised for enhancing image quality, particularly in improving contrast and illumination. Histogram applications are extensive in mapping, especially in satellite imagery analysis and digital cartography. In this study, the Kernel Density approach was utilised to refine the histogram by reducing noise and improving the representation of intensity distributions. By applying Kernel Density, the resulting histogram becomes smoother, facilitating the identification of optimal threshold values. Gonçalves et al. also noted that histogram-based thresholding methods, including those employing Kernel Density, can enhance image segmentation performance, particularly in applications involving less distinct objects, such as remote sensing.

Figure 5. Illustration of Histogram Values

Source: Zhu, et al., 2024

The kernel density analysis results for each variable were classified and weighted to produce a typology map of urban, peri-urban, and rural areas. Histogram-based thresholding techniques are adaptable depending on the objectives and characteristics of the data, with manual thresholding being one of the methods applied in this analysis. Manual thresholding involves the operator manually selecting a threshold value to separate objects from the background based on histogram analysis. This technique offers flexibility, allowing adjustments based on image characteristics such as pixel intensity and noise. In addition, Puspaningrum et al. highlighted that histogram-based thresholding could enhance image segmentation efficiency by reducing the number of iterations and overall processing time. While manual thresholding may be inherently subjective, standardised techniques such as Standardised Histogram Matching and Iterative Multi-Level Thresholding can mitigate bias. Standardised Histogram Matching compares data distributions (e.g., building density) against reference datasets using mathematical computations such as the Sum of Absolute Differences (SAD), thereby improving objectivity by considering the entire shape of the histogram rather than relying solely on visual estimation. As Wang (2016) emphasised, subjectivity in image processing can be productively managed through standardised histogram matching, which leverages human visual perception to enhance the relevance of spatial representations.

Concurrently, Iterative Multi-Level Thresholding employs a progressive series of criteria to refine classifications, ensuring accuracy by evaluating both local and global data characteristics. This methodology proves particularly effective for complex regions such as rural-urban transition zones, yielding more precise delineations compared to conventional methods. Through cross-validation and clearly defined convergence criteria, these techniques minimise subjectivity and improve analytical reliability (Ameer, 2019).

Result and Discussion

Heatmap Activity Accumulation

Density measurement using KDE of activity accumulation points in PPU Regency helps identify regions with high concentrations of urban activities. Nighttime Light (NTL) data are a unique remote sensing source capable of detecting low-intensity light, offering an effective method for identifying human activities (Y. Yang, Ma, et al., 2017). Compared to "daytime" imagery, the stark contrast between urban and rural regions makes NTL more suitable for characterising these regions (Zheng, Seto, et al., 2023). By analysing NTL data, a hotspot map can be generated to show strategic locations with the most intense community activities. The kernel density estimation results for NTL are summarised in Table 3.

Table 3. Statistics of Nighttime Light (NTL)

Sub-district	Mean	Max. Value	Min. Value
Penajam	10.8488	22	1
Babulu	9.89474	16	2
Waru	9.00	14	2
Sepaku	8.42857	13	3

Overall, the NTL distribution in PPU showed elevated values concentrated in Penajam Sub-district, with an average of 10.8488 (range: 1–22). In contrast, Sepaku Sub-district displayed the lowest NTL intensity, with a mean of 8.84615 (range: 4–13). These results highlight Penajam’s role as the administrative capital and governmental centre, correlating with significantly higher activity accumulation than other sub-districts. Meanwhile, Sepaku currently contains relatively inactive areas, as large parts of its territory have been designated for developing the new capital city, Ibu Kota Nusantara (IKN), resulting in reduced local activities. The NTL heatmap illustrating the spatial distribution of accumulated activity is presented in Figure 6.

The concentration of NTL in PPU was reasonable, as high NTL values (15–24) appeared in areas with government offices, urban settlements, and industries. Residential areas, especially denser urban settlements, tended to emit more light, indicating the intensity of human activities (Mellander, Lobo, et al., 2015; Rybnikova, 2022). Industrial areas also showed a strong correlation with NTL due to high light emissions from factories and industrial facilities, typically marked by intense activities and infrastructure (Mellander, Lobo, et al., 2015; Rybnikova, 2022). Notably, high NTL values were observed near government offices, which is uncommon in this type of analysis. This anomaly may be linked to PPU’s centralised government system, where all offices are located in a single area.

Figure 6. Activity Accumulation Heatmap

Heatmap Population Size and Density

The kernel density estimation (KDE) of population distribution and density points in PPU helped determine population density levels across the region. In this study, population and density were represented by Settlement Type (ST), divided into urban and rural settlements. Generally, urban settlements had larger populations than rural areas, influenced by factors such as migration, economic opportunities, and social facilities (Cirella, Mwangi, et al., 2022). The KDE results for settlement typology are systematically summarised in Table 4.

Table 4. Statistics of Population Size and Density (ST)

Sub-district	Mean	Max. Value	Min. Value
Penajam	17.9347	45	0
Babulu	4.17391	11	0
Waru	13.7195	28	0
Sepaku	1.00	2	0

Table 4 reveals a clear concentration of population distribution in Penajam Sub-district, with an average value of 17.93. This pattern can be linked to Penajam’s role as the administrative and economic centre of PPU Regency, supported by its status as the governmental hub and its strategic port infrastructure connecting PPU to neighbouring cities in East Kalimantan Province. These analyses of population size and density further confirms that high-density areas align with regions featuring a significant concentration of built-up structures, typically classified as urban zones.

Additional detail is provided by the spatial heatmap in Figure 7, which illustrates the uneven distribution of population density. The heatmap highlights Penajam’s position as a core activity node, in stark contrast to peripheral sub-districts that show lower demographic engagement, partly due to the prioritisation of the Nusantara Capital (IKN) project in nearby areas.

Figure 7. Population Size and Density Heatmap

According to Figure 7, Babulu Sub-district had a fairly large residential area (1,461 Ha). However, over 90% of this area is classified as rural, resulting in lower building and population density than the urban settlements in Penajam and Waru Sub-districts. The development of urban settlements in PPU mainly driven by key infrastructure, such as the port in Balikpapan Bay. Urban settlement distribution in the regency tended to follow existing road networks. These findings align with Darwish, Elghazali, et al. (2007), who noted that road construction could stimulate urban settlement growth, especially in developing countries. Similarly, in Spain, new highway construction led to significant land use changes, converting rural land for urban purposes and facilitating development (Garcia-López, Sole-Olle, et al., 2014).

Heatmap Connectivity

The KDE of connectivity point distribution in PPU Regency provided insights into connectivity levels and community movement patterns. The analysis of Network Centrality (NC) helped identify regions with transportation nodes that are well connected within the network. This offers an overview of key nodes that play a crucial role in the efficiency and interconnections of the urban network. The KDE results from the NC analysis are shown in Table 5.

Table 5. Statistics of Connectivity (NC)

Sub-district	Mean	Max. Value	Min. Value
Penajam	41.415	189	0
Babulu	52.9293	328	0
Waru	26.7347	106	0
Sepaku	16.1892	49	0

NC effectively captures connectivity in urban environments by measuring the importance of nodes (such as roads or intersections) within the network (Porta, Crucitti, et al., 2006). A higher concentration of these nodes indicates greater complexity and accessibility in the road network, often correlating with higher activity levels. Based on the analysis, Babulu Sub-district recorded the highest average NC value (52.9293), while Sepaku showed the lowest (16.1892). These findings suggest that Babulu has the highest average level of connectivity within PPU Regency. The spatial distribution of NC values, illustrated in a connectivity heatmap, is presented in Figure 8.

Figure 8. Connectivity Heatmap

The analysis showed that Babulu ranked highest in terms of connectivity, due to its extensive residential areas, which require a well-connected road network. These results support the theory that higher NC levels are linked to more intensive residential development (Yin, Liu, et al., 2022). In contrast, Sepaku had a low connectivity value, reflecting its limited residential and cultivation areas. This contributed to lower economic and social activity, reducing the need for a broader and more integrated road network.

Heatmap Land Use Characteristic

The KDE of Land Use Characteristic (LUT) distribution in PPU helped identify the pattern and intensity of land use across the region. Density level analysis of LUT can pinpoint areas with specific concentrations and offer insight into related socio-economic dynamics. LUT was categorised into four types with different scores: unused land, forest, water body, grassland, cultivated land, garden plot, transportation land, residential area, and industrial land. Unused land had the lowest score due to minimal activity, while transportation, residential areas, and industrial land recorded the highest scores because of intense activity (Peng, Zhao, et al., 2016). Detailed statistics are presented in Table 6.

Table 6. Statistics of Land Use Characteristic (LUT)

Sub-district	Mean	Max. Value	Min. Value
Penajam	18.2928	42	0
Babulu	14.4415	34	2
Waru	14.6829	31	3
Sepaku	15.3288	27	2

The highest average LUT values in PPU were found in Penajam (18.2928), while Babulu recorded the lowest (14.4415). The KDE analysis of LUT revealed a high density level in residential, industrial, and transportation areas. Specifically, Penajam and Babulu Sub-districts emerged as regions with significant land use density, reflecting the concentration of economic activity and population in these areas. The heatmap of these findings is shown in Figure 9.

Figure 9. Land Use Characteristic Heatmap

The high score for residential areas was linked to the rising trend of urbanisation. Populations in urban regions tended to grow alongside increased demand for housing. As noted by Chithra, Anilkumar, et al. (2015), this trend highlights the importance of residential land in shaping urban land use, particularly in rapidly growing areas. In developing regions, especially sub-districts experiencing fast population growth, residential land often dominated land use patterns.

The industrial LUT also showed a high score, underlining the key role of industry in attracting labour and driving urbanisation (Djirimu, Taqwa, et al., 2024; Henderson, Kuncoro, et al., 1995). Industrial zones typically draw a workforce, which supports the growth of surrounding urban areas. These results stress the need for effective zoning to ensure industrial expansion does not disrupt residential spaces.

The transportation LUT played a key role in shaping the structure and pattern of urbanisation. Transportation infrastructure, including highways, railways, and terminals, supported mobility and influenced the spatial distribution of economic and social activities within cities. The development of transportation networks often drives land use changes, increasing urban activity along major routes (Helber, Bischke, et al., 2019; Wegener and Fuerst, 2004). Thus, the stronger a region’s connectivity to an economic hub through its transportation networks, the greater its potential to develop into a densely urbanised area. In summary, the KDE analysis demonstrated the relationship between urbanisation factors, industrial growth, and transportation infrastructure in shaping land use patterns in the PPU region.

Heatmap Government Services

Density measurement using KDE of Government Services (GS) distribution in PPU revealed regions with a high concentration of facilities. GS density is crucial for identifying urban functional areas, as it reflects the intensity and variety of activities within a region (C. Huang, Xiao, et al., 2022). In some cases, government offices record the highest scores due to the centralisation of administrative functions, which are typically located in urban centres to ensure accessibility and civic engagement (Tilman, 2024). A detailed breakdown of GS statistics is provided in Table 7.

Table 7. Statistics of Government Services (GS)

Sub-district	Mean	Max. Value	Min. Value
Penajam	16.0325	47	0
Babulu	11.7295	28	0
Waru	4.57143	12	0

Penajam Sub-district (16.0325) had the highest average value, while Waru (4.57143) had the lowest. The KDE analysis of GS showed a higher density in areas near government offices and health facilities, particularly in urban regions. Urban centres often have a greater concentration of hospitals, including specialised facilities such as children's hospitals and advanced surgical care centres, to meet the needs of denser populations. In contrast, rural areas tend to have fewer hospitals and specialised health services (Z. Li, Ho, et al., 2024).

Urban regions also feature more educational facilities due to higher population density and demand for schooling (Pallini, Korolija, et al., 2020). Additionally, the concentration of trade and service facilities is typically greater, indicating how urbanisation can support the provision of public and commercial amenities.

Figure 10. Government Services Heatmap

The distribution of government services in PPU remained highly centralised in Penajam Sub-district, the regency capital. As a result, there is an uneven spread of public services, with non-urban regions having limited access to government and public facilities. This disparity presents a challenge for improving community welfare across the regency, particularly in ensuring equal access to basic services such as health, education, and commerce. The heatmap of these findings is shown in Figure 10.

Rural-Urban-Regions of Penajam Paser Utara

The determination of RUR typology using manual thresholding aims to establish boundary values that separate the three typologies. In the histogram, blue vertical lines indicate thresholds distinguishing rural from peri-urban at approximately a value of 6, and peri-urban from urban at around 15. were selected based on data distribution analysis and field observations, taking into account factors such as density levels and the intensity of human activity. The resulting typology map validates the thresholding outcomes by dividing the Penajam Paser Utara Regency into three distinct zones, as shown in Figure 11.

Figure 11. Manual Thresholding

Although manual histogram-based thresholding has inherent limitations due to the subjectivity involved in selecting thresholds, this approach can still produce a reliable RUR (Rural-Urban-Rural) typology when supported by robust strategies to mitigate subjectivity (Ameer, 2019). It is critical to address subjectivity by integrating objective strategies, including:

1.    1. Standardised Histogram Matching: This method compares the kernel density distribution of variables (such as building density) with the final RUR typology using the sum of absolute differences (Ameer, 2019). Rather than focusing on individual features, it prioritises the overall histogram shape, providing a more quantitative basis for threshold selection.
2.    2. Iterative or Multi-Level Thresholding: Applying thresholds repeatedly or hierarchically refines the classification process, ensuring consistency between results and ground conditions.

As Wang (2016) notes, subjectivity in image processing can be effectively managed through techniques like standardised histogram matching, which leverage human visual perception to improve the relevance of spatial representation.

By combining these approaches, manual thresholding retains the flexibility of visual interpretation (aligning with Wang’s theory) while meeting objectivity standards through data- and algorithm-driven validation. Consequently, the derived RUR typology gains greater reliability for spatial policy analysis. Each typology reflects values obtained from image analysis or other statistical data.

Moreover, kernel density processing of activity accumulation, population size and density, connectivity, land use characteristics, and government services contributed to the delineation of the RUR typology (urban, peri-urban, and rural) in PPU. The resulting typology maps are presented in Figures 12 and Figures 13.

Figure 12. Rural-Urban-Regions of Penajam Paser Utara

Figure 13. Comparison between Urban Regions and Existing Condition

Figure 12 and Figure 13 show that urban regions in PPU remain limited and generally follow the existing road network pattern. In other words, urbanisation in these areas is still in its early stages, with development spreading along major transportation routes rather than expanding evenly. Additionally, the distribution of RUR, particularly in urban and peri-urban regions, appears scattered and not concentrated around a single centre. This aligns with the polycentric RUR theory, which suggests that peri-urban areas no longer merely serve as transition zones surrounding urban regions but instead develop into independent territories with distinct characteristics (Ravetz, Fertner, et al., 2013).

Peri-urban regions now play a more dynamic role, with unique social, economic, and ecological functions that are not necessarily tied to the central city (Sareen and Haque, 2023). This reflects structural shifts in the development of peri-urban areas, which often occur organically and unexpectedly. Factors such as population growth, land use changes, infrastructure development, and economic pressures all contribute to the dynamic nature of peri-urban regions, where rapid and sometimes inevitable transformations take place.

As these peri-urban regions develop, their transition into urban areas can happen more quickly than anticipated. Previous research has shown that rapid changes in peri-urban regions often lead to increased urbanisation (Sadiki and Ramutsindela, 2002). Thus, areas initially classified as peri-urban can become urban in a relatively short time.

The distribution of RUR typologies in this study was also consistent with recent findings from Wuhan, China, where peri-urban regions were not always adjacent to urban areas. In some cases, peri-urban zones extended far from the city centre, reflecting a more complex polycentric development pattern (Sun, Liu, et al., 2023). Similarly, in Indonesia, particularly in the Bandung Metropolitan Area, peri-urban zones are not always located next to urban areas (Budiyantini and Pratiwi, 2016). This further underscores the important role of these regions in broader regional development, serving not only as buffer zones but also as areas with distinct dynamics and identities. A detailed breakdown of RUR typology distribution by sub-district is shown in Table 8.

Table 8. Distribution of RUR Typology

Sub-district	Rural-Urban-Regions
	Urban (Ha)	Peri-Urban (Ha)	Rural (Ha)
Penajam	787.02	5,690.9	82,227.8
Babulu	247.52	6,362.03	26,762.9
Waru	-	1,396.92	43,539.7
Sepaku	-	160.70	36,368

Table 8 shows the distribution of regions by sub-district, categorised into urban, peri-urban, and rural (in hectares). Penajam contained 787.02 hectares of urban areas, 5,690.9 hectares of peri-urban, and 82,227.8 hectares of rural land. Babulu had smaller urban regions at 247.52 hectares but a larger peri-urban area than Penajam at 6,362.03 hectares, along with 26,762.9 hectares of rural land. Meanwhile, Waru recorded no urban areas but had 1,396.92 hectares of peri-urban and 43,539.7 hectares of rural land, confirming the dominance of rural activities. Similarly, Sepaku had no urban regions, with 160.70 hectares classified as peri-urban and 36,368 hectares as rural. Overall, the region was predominantly rural with limited urban areas, highlighting a focus on agriculture, forestry, or nature conservation rather than urban development. The analysis showed that the distribution of RUR typologies did not adhere strictly to administrative boundaries. This aligns with earlier studies, which suggest that urban development should be assessed not only through administrative units but also through functional urban units (Wunarlan, Soetomo, et al., 2020). Thus, these findings support the view that urban classification is becoming more dynamic, offering a more accurate representation of urbanised regions (Ottensmann, 2019). The rural areas identified through this analysis also matched well with the supporting variable data. Sepaku Sub-district, in particular, emerged as an area dominated by rural characteristics. Currently, Sepaku lacks government services because it has no residential areas, consistent with studies indicating that rural regions often have limited access to education and healthcare services (Pateman, 2011). By contrast, Penajam Sub-district, which has the highest population in Penajam Paser Utara (PPU) Regency, also has the largest distribution of urban areas. This is due to the wide range of socio-economic activities in Penajam. Moreover, the PPU Regency government is located in this sub-district, aligning with other variables such as ST, LUT, NTL, and NC. This demonstrates the significant role of government centres in shaping urban areas. It supports findings that the centralisation of political power often places government hubs within urban regions, particularly in countries with extensive urban infrastructure and concentration (Krugman, 1994). In Indonesia, for example, government centres in urban areas are commonly situated at the core, marked by high population density, accessibility, and proximity to open spaces, as observed in six provincial capitals (Romdhoni and Rashid, 2021).

This study successfully applied the RUR typology determination method by integrating five variables: LUT, ST, NC, NTL, and GS. The findings confirmed that RUR classification is not confined to administrative boundaries, supporting earlier studies that emphasise the need for a comprehensive depiction of highly dynamic RUR areas beyond simple geographic borders (Ghosh and Khatun, 2022). As a case study, PPU Regency illustrates a region with high RUR dynamics due to its proximity to the development of IKN. This mirrors findings from North Cianjur, where urban expansion from metropolitan areas like Jakarta and Bandung led to urban sprawl and environmental challenges (Jatayu, Rustiadi, et al., 2020).

The method used in this study effectively captured the RUR typology in highly dynamic regions, demonstrating the need for a more comprehensive assessment. It showed the successful integration of NC with government GS, as reflected in the classification of the northern part of Penajam Sub-district as peri-urban, illustrated in Figure 14. This study also validated the simultaneous use of LUT, ST, NC, NTL, and GS as variables, offering a robust approach for classifying RUR in dynamic regions such as PPU Regency. This marks an improvement over existing methods, which often consider these variables separately.

Figure 14. Comparison of Variables (Sample)

The methodological differences in this study can be compared to the work of Sun, Liu, et al. (2023), who used road density and taxi trips to describe regional connectivity. However, taxi trips tend to create inequalities in rural areas, as taxis predominantly operate in urban regions. Taxi trips, which mainly occur within cities, do not capture the characteristics of rural areas. Urban taxi trips often follow specific patterns, such as high frequency during peak hours, which fail to represent rural travel dynamics.

This contrasts with the use of NC in this study, which relies on degree centrality data. By integrating NC with GS, the study offers a more complete depiction of connectivity. This method has been shown to reduce disparities between regions with taxi services and those without.

Understanding spatial structure is fundamental for responsive policy formulation and governance, enabling tailored approaches that address the unique needs of urban, suburban, and rural areas, particularly in transitional zones with complex socio-economic dynamics (L. Li, Tang, et al., 2023; Sahana, Ravetz, et al., 2023). Accurate spatial identification supports effective urban planning and development by tackling challenges such as urban sprawl, infrastructure gaps, and land-use conflicts. Emerging techniques like neural embeddings reveal segregation patterns, guiding more equitable planning decisions (Fan et al., 2024). This also ensures efficient resource allocation by prioritising infrastructure investments, public services, and environmental conservation based on distinct urban-suburban-rural characteristics (Cruz-Bello, Galeana-Pizaña, et al., 2023). Furthermore, spatial analysis underpins sustainable environmental management by controlling unchecked urban expansion, preserving green spaces, and mitigating ecological impacts, for example through targeted strategies to protect ecosystem services in peri-urban areas (Cruz-Bello, Galeana-Pizaña, et al., 2023).

The delineation of rural, peri-urban, and urban areas in PPU, which directly borders Indonesia’s new capital city (IKN), is critically important given the region’s rapid development dynamics. These dynamics can lead to spatial inequalities (Weisbuch, 2013), resource pressures (Prasad Mishra, Mondal, et al., 2022), and socioeconomic structural changes (Mertl and Valenčík, 2016). Clear classification enables precise spatial planning to manage urban growth, preserve essential rural functions, and optimise peri-urban zones as transitional buffers between urban and rural needs. The findings provide valuable insights for policymakers in designing spatial planning and development strategies, especially in anticipating and mitigating potential negative impacts often associated with rapid urbanisation in developing countries (Mohammad and Odeh, 2024), such as uncontrolled land-use changes and environmental pressures. The RUR typology can serve as a reference for targeted interventions to promote balanced regional growth. This identification also supports evidence-based policymaking for infrastructure allocation, public service delivery, and environmental conservation, while reducing the risks of urban sprawl and land degradation frequently seen around new capital regions. Consequently, mapping PPU's rural, peri-urban, and urban zones offers a strategic foundation for advancing balanced development in line with IKN’s sustainability vision. Based on these findings, future research should prioritise the development of sustainable urban–rural linkage mechanisms to foster balanced regional growth and mitigate disparities driven by urbanisation. This research direction would yield policy-relevant insights for advancing integrated spatial planning frameworks.

Conclusion

In conclusion, this study showed that the analysed areas were predominantly rural, with substantial rural expanses in each sub-district, while urban regions remained limited. Penajam and Babulu Sub-districts had larger peri-urban than urban zones, indicating potential for future urbanisation. Meanwhile, Waru and Sepaku Sub-districts lacked urban area data, confirming their primary activities are rural. Overall, the distribution of the RUR typology was not bound by administrative borders but instead reflected geographical characteristics and land use patterns, often shaped by agriculture, forestry, or nature conservation. This study defined the RUR typology to better understand existing spatial structures in the study area, particularly in rapidly developing regions like PPU. Future research should focus on developing sustainable urban-rural linkage strategies to ensure balanced growth while addressing potential inequalities resulting from urbanisation.

Previous study encountered difficulties in accurately assessing connectivity in non-urban areas using taxi trip data. This study, however, effectively mitigated this limitation by employing Network Centrality (NC) as a metric for connectivity, thereby providing a robust measure of the interconnectivity of the road network throughout the study area. This method provided a more refined understanding of how rural, peri-urban, and urban regions are connected, especially in non-urban areas where taxi trip data may not adequately reflect real movement and activity patterns.

This study offered new evidence for applying advanced kernel density models that incorporated multiple data sources to represent key land use and connectivity features. Specifically, the analysis integrated datasets reflecting Land Use Characteristic (Land Use Type), Population Size and Density (Settlement Type), Activity Accumulation (Nighttime Light), Government Services, and Connectivity (Network Centrality). Combining these multiple data layers created a comprehensive framework that more accurately depicted how different regions were utilised, developed, and interconnected.

In addition to this innovative use of data, the variables were streamlined, demonstrating how datasets like population size and density could be simplified into broader categories such as urban and rural settlement types. This simplification allowed for a clearer understanding of spatial relationships and land use patterns while maintaining rich data for analysis. Through these methodological advancements, the study contributed to the growing body of knowledge on RUR connectivity and provided a robust framework for future research and policymaking in regional planning and development.

The classification of RUR in the areas surrounding IKN provides a critical foundation for strategic spatial planning, enabling clear functional zoning between urban cores, peri-urban buffers, and supporting rural territories. This systematic delineation facilitates efficient allocation of resources and infrastructure while reducing risks of land fragmentation and ecological pressures. By preserving the distinct characteristics of each zone, RUR classification promotes sustainable urban–rural integration, strengthening food security through the protection of agricultural land and optimising regional connectivity—both essential for achieving IKN’s vision of a smart, low-carbon city. Ultimately, this approach not only helps prevent development disparities between PPU and IKN but also fosters a functional symbiosis that supports the new capital’s long-term sustainability goals.

This study has several limitations, mainly related to data availability. In particular, nighttime light data were only accessible for certain periods and may vary depending on when they were collected, potentially affecting the consistency and temporal representativeness of the findings. Furthermore, this study did not include a temporal comparison of RUR typologies due to the highly dynamic nature of PPU Regency, which is largely influenced by the ongoing construction of Indonesia’s new capital city (IKN). This gap presents an opportunity for future research to reassess the typology within the next five to ten years, once development has progressed further. In addition, future studies should prioritise the development of sustainable urban–rural linkage strategies, which are crucial for the effective evolution of RUR systems (Rahmoun and Zhao, 2024).

Author Contributions

Conceptualisation, R.A.P. and I.R.; methodology, R.A.P., M.Q.P. and M.A; software, R.F and V.A.F.S.; investigation, R.A.P., O.L.S. and P.W.B.; resources, P.W.B.; data curation, M.Q.P. and M.A.; writing—original draft preparation, R.A.P., M.Q.P. and M.A.; writing—review and editing, R.A.P., A.N.D and M.Q.P.; supervision, I.R. All authors have read and agreed to the published version of the manuscript.

Ethics Declaration

The authors declare that they have no conflicts of interest regarding the publication of the paper.

Acknowledgments

The authors wish to express their gratitude to the students of the Institut Teknologi Kalimantan for their invaluable assistance during the fieldwork. Additionally, we extend our appreciation to the residents of Penajam Paser Utara Regency for their support throughout this research. We also acknowledge the anonymous reviewers for their insightful comments and constructive suggestions that significantly enhanced the quality of this manuscript.

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