Analysis of the Potential of Commercial Corridor Based on Consumer Movement Interactions in Central Jakarta

In the evolving landscape of modern commercial development, the potential of Jakarta’s shopping center corridors has emerged as a key area of interest. Consumer lifestyle behavior shifts, technological advancements, and design innovations have shaped new trends in the retail industry. The development of modern shopping centers has brought significant changes in consumer movement patterns in various metropolitan cities, including Central Jakarta. Utilizing empirical data from bustling shopping centers, the research employs the Huff and Gravity models to analyze consumer movement patterns. These models offer a nuanced understanding of how factors like accessibility, population density, and urban design influence consumer choices. The results show that ITC Roxy has the highest probability of consumer visits, while H. M. Saleh Ishak Street, Sutan Syahrir Street, Dr GSSJ Ratulangi Street, Menteng Raya Street, Kramat Kwitang Street, and Pasar Senen Street have the highest interaction volumes due to their proximity to arterial roads and collectors. The results reveal a significant shift in consumer movement behavior, driven by urban development and demographic changes, underscoring the urgency for adaptive commercial planning.


Introduction
Modern shopping centers have evolved to become significant drivers of shopping behavior, social patterns, and cultural norms [1].Their image, size, amenities, and location significantly influence consumer desire and intention to visit [2].Modern shopping centers have evolved into multifunctional spaces, offering retail opportunities, entertainment, relaxation, and educational experiences [3].Shopping malls are significant components of a sustainable city, contributing to the economy and providing spaces for social activities.However, it is crucial to consider the potential negative impacts, such as traffic congestion, that can result from their development [4].To address these challenges, city planners and designers must carefully consider the impact of shopping centers on the urban streetscape [5].This approach is in line with the growing focus on the effects of the shopping experience on consumer behavior and the significance of providing a pleasant customer experience [6].The study of the influence of hedonic and utilitarian shopping value on consumer behavioral intention among youth mall shoppers emphasizes the significance of understanding consumer preferences and behaviors in shaping modern shopping centers [7].
Central Jakarta is an administrative government zone, a shopping center, and for businesses along MH.Thamrin Street and near the State Palace, various shopping centers cater to the diverse needs of locals and visitors [8].According to Google Maps, Central Jakarta has 14 modern shopping centers, IOP Publishing doi:10.1088/1755-1315/1353/1/012030 2 verified by field surveys.Factors that affect location determination in Central Jakarta are the number of residents, the area of malls (modern shopping centers), and road networks [9].The determinants influencing site selection within Central Jakarta also encompass demographic factors, such as population density, retail floor area, and the road network layout [10].Determining trade location in Central Jakarta may only partially align with existing theories such as Christaller, Losch, and Webber's [11].The gravity model is a valuable tool for understanding consumer behavior in shopping centers.It has been used to estimate sales, test the importance of retail agglomeration and proximity to competition, and survey actual consumers, providing insights into their behavior and preferences when visiting shopping centers [1], [12], [13], [14].From the application of the Huff model and Gravity Model in this study, a probability value will be obtained in each RT (Neighbourhood Association) in Central Jakarta City against all shopping centers in Central Jakarta City and movement patterns in all existing shopping centers [15].This research area focuses on the Central Jakarta Administration City located in DKI Jakarta Province, which eight sub-districts consisting of Tanah Abang, Senen, Gambir, Menteng, Johar Baru, Kemayoran, Sawah Besar, and Cempaka Putih with a focus on the number of RTs in each subdistrict.
Shopping Centers Attractiveness A shopping mall is a commercial area with multiple shops and retail facilities in one convenient location.Different shopping centers include malls, regional shopping malls, outlets, and convenience centers, each offering various consumer products and services [16], [17].Several factors, such as architectural design, variety of stores, comfort features, and overall atmosphere, influence shopping centers' appeal.These elements shape consumer evaluations and their inclination to revisit shopping centers [18].The retail industry has undergone significant changes due to growing competition among regional-scale malls, which has impacted the design and tenants of these malls in attracting retailers and consumers [19], [20].It is widely accepted that the location of a shopping mall plays a crucial role in its success.A well-chosen location can attract a large consumer base and significantly impact profits.On the other hand, an inappropriate location can harm the mall's performance [21], [22].Alfred Weber's location theory, developed in 1909 to describe industrial locations, can also be applied to shopping malls.The concept of accumulation in the theory suggests that industries often cluster together to benefit from reduced logistics costs and access to a skilled labor market [23].Shopping malls are often strategically located in areas with a wide variety of retail facilities to attract more consumers, following Webber's location theory, where transportation costs play a crucial role in industrial location decisions [24].Shopping centers are typically situated in easily accessible areas near main roads or public transportation routes to facilitate consumer visits [25].Consumer Interaction Consumer interaction with retail spaces, such as shopping centers and stores, is crucial for business success in the competitive global market [26].Understanding consumer behavior guides entrepreneurs toward competitiveness by ensuring effective strategies for reaching target consumers [27].This involves activities like seeking product information, comparing prices, making purchasing decisions, and site analysis to evaluate feasibility based on location and accessibility [28].Factors influencing location choice include proximity, thoroughfare convenience, and nearby activities supported by models developed by Alfred Weber.Accessible areas are preferred to increase consumer interaction with shopping centers or stores [29].By understanding the influences on consumer behavior and considering the concepts of accumulation and location theory, retail businesses can make informed decisions about where to establish [30].

Methodology 3.1. Data Collection Methods
The data collection method is a secondary survey, which collects data from existing sources, such as printed sources, reports, publications, databases, or other electronic sources.The data sought is the number of residents based on RTs (Residential Neighbourhood) and the location of modern shopping centers in Central Jakarta.Where population data is available on the DKI Jakarta Population geodatabase data, and current shopping center location data in Central Jakarta can be seen on Google Maps with field validation.This research uses travel time as an impedance representative for each road network to divide where an optimal route.These are the type of road and its range of free flow speed [31], [32]:

Type
Range of Free Typical Free Flow

Data Analysis Methods
This study aims to determine the probability value of residents going to a modern shopping center in Central Jakarta and the pattern of population movement between modern shopping centers.Data obtained shows 14 modern shopping centers and 4847 RTs in Central Jakarta.After the data is received, data processing is carried out for each target following their respective analysis techniques.

Estimates Store Attractiveness
This modeling is used to find the probability value of each resident based on RT (Residential Neighbourhood) going to a modern shopping center.In this analysis, a comparison was made between the area of a modern shopping center and the distance from a location to each modern shopping center.The comparison is based on a shopping center with a larger area, and the services available will also be more varied.Each mall's probability value is obtained from the analysis calculation results for each location.These data are processed using UrbanSCAD software.Furthermore, calculations are carried out using the market share formula to find the total number of visitors expected to come based on the probability value that has been analyzed and the number of residents of each RT (Residential Neighbourhood) [33].The formula is as follows:

Flow
Where:  : The probability of a consumer at a given origin i traveling to a particular shopping center j  : The size of a shopping center j (measured in terms of the square footage of the selling area devoted to the sale of a particular class of goods)  : indicates the travel time involved in getting from a consumer's travel base i to a given shopping center j  : is a parameter that is to be estimated empirically reflecting the effect of travel time on various kinds of shopping trips  =  ×  Where:  : The expected number of consumers at i that are likely to travel to shopper center j  : the number of consumers at i.So it is found the number of residents who tend to go to a shopping center, where the value will be added and become one value for each existing shopping center.

Compute Potential Consumer Movements
At this stage, gravity modeling analysis is carried out using GraviGIS with gravity assessment in the form of Interaction Volume-NS to obtain which movement pattern is the densest from each between shopping centers by considering the function of the speed of each type of road section (impedance).This model adapts to compute the breaking points of retail influence between two competing stores.This information can be transformed into a network with high consumer interaction [33].
This study uses a gravity model to calculate the movement interaction based on road network interactions.Calculations are performed by considering accessibility, destination attractivness, and impedance functions from the origin points to the destination points.The adapted gravity model equation is as follows [34]: Where: i : number of movements from i to d that charged to the road.i ;  : balancing factors for each origin and destination, in this case is total of customer.i : total number of movements from the origin zone  : total number of movements to the destination zone (i) : impedance value from each road level based on table 2 5 A data normalization technique transforms values into a specific scale during the data analysis.This process involves converting interaction values into a range of 0-1.Normalizing data establishes a uniform distribution of values within the dataset, which aids in interpreting analysis results and simplifies the comprehension of inter-variable user comparisons.Normalized data also facilitates a clearer understanding of the comparisons between variables [33].The formula for data normalization is as follows:

Result and Discussion
In the analysis conducted, there are several visualizations of the data used in this paper.

Figure 2. Road Network and Road Network Classification
The map shows the road network used for analysis, along with its classification.The road network used for analysis includes road networks of Arterial Roads, Collector Roads, and Local Roads.Meanwhile, the Environmental Road is not used because its designation is not for connecting roads between one region and another.Figure 2 also shows the road classification and the speed limit for each road network classification, which is used as impedance in this analysis.

Figure 3. Administrative Map and Its Population
Figure 3 shows an administrative map in which there is also a population for each region.The basis of this population is used to analyze the probability of the number of people who will visit shopping centers in Central Jakarta.

Figure 4. Shopping Center Distribution Map
Figure 4 shows a map of the distribution of shopping centers, which in this analysis are used as destinations for residents in Central Jakarta.In addition to being a destination, this shopping center is also the basis for determining the density of existing road sections in Central Jakarta by considering gravity model analysis.

4.1.
Estimates Store Attractiveness From the calculation of Huff model, the probability value of residents in each RT located in Central Jakarta is obtained against each modern shopping center in Central Jakarta.After getting all probability values in each RT against all modern shopping centers, then overlay between these values.The overlay results show RT areas with the highest to lowest probability in each modern shopping center.
These results show that the existence of modern shopping centers affects the likelihood of people visiting.Factors that influence the size of the probability value of people visiting modern shopping centers are based on the distance and area of the shopping center, affecting the number of shops and goods sold in that place.The modern shopping center ITC Cempaka Mas has the highest probability value; this is undoubtedly influenced by various factors, such as ITC Cempaka Mas is located close to community settlements, completeness of facilities or goods available, and building area.
Furthermore, Huff's modeling can find the value related to the number of consumers expected to come using the market share formula.Like the existing market share formula, to get the results of the expected number of consumers who visit each modern shopping center, calculations are carried out using the market share formula that multiplies the probability of consumers of each modern shopping center with the population of each RT.The results of these calculations can be seen in Table 4 below, which shows the results of calculating the probability of consumers visiting each modern shopping centre and the expected number of consumers who come to each shopping centre.From the results of the market share calculation, it can be seen that the highest number of expected consumers in modern shopping centers was found at ITC Cempaka Mas, with an estimated number of consumers of 246.159 people, then followed by Grand Indonesia as the second highest and ITC Cempaka Mas as the third highest with each expected consumer of 208.940 people and 149.037 people.This can be influenced by the location of the modern shopping center, which is very close to residential areas with high population density.
The distribution of each modern shopping center can be seen in Figure 5, which visualizes the expected number of consumers who visit each.Potential Consumer Movements In this stage will be used gravity model, movement patterns will be obtained in each shopping center.In determining interactions in all shopping centers using data from market share calculations and road network data in the Central Jakarta area.The type of road network that exists, then weighting the maximum speed limit as impedance, so that the main focus of movement patterns occurs on arterial and collector roads.

Conclusion
Based on the results of the analysis and discussion that has been carried out, this study produces conclusions that the Huff model calculates the probability of residents visiting each modern shopping center, influenced by factors such as distance and area of the shopping center.The analysis shows that ITC Cempaka Mas has the highest probability value, controlled by proximity to community settlements and available facilities.The huff model also determines the number of expected visitors, with ITC Cempaka Mas having the highest value.This is also undoubtedly influenced by the location of modern shopping centers close to community settlements with high population density.
The gravity modeling reveals movement patterns in each shopping center, with the highest interaction patterns occurring in the corridor connecting Plaza Indonesia with Plaza Atrium, with the highest on its road in H. M. Saleh Ishak Street.This can happen because the shopping centers are connected to the road network through arterial roads and collector roads, so there is a strong interaction in each shopping center.From the results of movement patterns with high interaction, there will be a buildup on the road, so recommendations are obtained by increasing the intensity of public transportation use and optimizing road sections to increase travel effectiveness.

Figure 5 .
Figure 5. Shopping Center Visitor Probability Map 4.2.Potential Consumer Movements In this stage will be used gravity model, movement patterns will be obtained in each shopping center.In determining interactions in all shopping centers using data from market share calculations and road network data in the Central Jakarta area.The type of road network that exists, then weighting the maximum speed limit as impedance, so that the main focus of movement patterns occurs on arterial and collector roads.

Table 1 .
Data Requirements and Data Sources

Table 3 .
Expected Number of Consumers

Table 4
explains the characteristics of each road section with the highest interaction level, as previously described. 9

Table 4 .
The Characteristics of Highest Commercial Corridors Interaction