## 1 Introduction  
By relying on the technological development of the \"Internet+\", ride-hailing has become an important part of the modern public transport system [1]. The emergence of Baidu, Gaode, Didi, Uber, and many other ride-hailing software products has brought great convenience to people's lives. In some American cities, compared with private cars, ride-hailing is conducive to reducing traffic congestion [2] and environmental pollution causedby exhaust emissions, which is of great significance for promoting sustainable development and mitigating traffic congestion [3]. However, it also brings negative impacts such as traffic congestion and low travel efficiency in some cities, such as Manhattan District in New York [4]. Understanding the impact mechanism of the built environment on ride-hailing ridership can provide guidance for ride-hailing management and land use planning. The factors that affect the ridership of ride-hailing are complex [5]. Some scholars have studied the issue from different perspectives, including the impact of public transport, walking and cycling [6], user characteristics [7], the service attitude of ride-hailing [8], availability [9], etc., on the ridership of ride-hailing. Some scholars have begun to pay attention to the impact of built-environment variables such as transportation, land use, and urban design on ride-hailing travel [10]. Based on the \"3D\" dimension proposed by [PERSON] [11], namely density, diversity, and design, [PERSON] added two categories, namely distance to transit and destination accessibility, and proposed a \"5D\" theory (density, diversity, design, distance to transit, and destination accessibility) [12]. \"Density\" is measured as the variable of interest per unit of area; \"diversity\" measures pertain to the number of different land uses in a given area and the degree to which they are represented in land area, floor area, or employment; \"design\" includes street network characteristics within an area; \"distance to transit\" is usually measured as an average of the shortest street routes from a residence or workplace in an area to the nearest rail transit station or bus stop; and \"destination accessibility\" measures the ease of access to trip attractions. The existing research mainly selects explanatory variables based on the \"3D\" built environment theory. [PERSON] et al. selected explanatory variables from accessibility, land use, population distribution, and facilities [13]; [PERSON] et al. selected explanatory variables from traffic facilities and land use [14]; and [PERSON] et al. selected explanatory variables based on the \"5D\" built-environment theory from four aspects: density, diversity, urban design, and traffic distance. In addition, a housing price variable was added [15], but the destination accessibility variables were not considered as influencing factors. However, few studies have considered the impact of the explanatory variables of the \"5D\" dimension of the built environment on the ridership of ride-hailing. The scale difference for the data aggregation of ride-hailing ridership and built-environment variables will affect the analysis results. The spatial object exists only after a set of real collected data is aggregated to spatial units [16]. The first step of many spatial research problems is to divide the aggregation area [17]. Many types of big geo-data are used for urban studies where the size of the analysis unit determines the amount of data to be included and affects the value of each unit, and it is more important to look into the scale effect of big geo-data [18]. Therefore, the size (i.e., scale) and shape of the aggregation area will affect the analysis results. [PERSON] describes this phenomenon as a modifiable area unit problem, which refers to the problem that the analysis results vary with the definition of the basic area units [19]. The MAUP problem has two aspects.