Using Building Floor Space for Station Area Population and Employment Estimation

Abstract

Analyzing population and employment sizes at the local finer geographic scale of transit station areas offers valuable insights for cities in terms of developing better decision-making skills to support transit-oriented development. Commonly, the station area population and employment have been derived from census tract or even block data. Unfortunately, such detailed census data are hardly available and difficult to access in cities of developing countries. To address this problem, this paper explores an alternative technique in remote estimation of population and employment by using building floor space derived from an official administrative geographic information system (GIS) dataset. Based on the assumption that building floor space is a proxy to a number of residents and workers, we investigate to what extent they can be used for estimating the station area population and employment. To assess the model, we employ five station areas with heterogeneous environments in Tokyo as our empirical case study. The estimated population and employment are validated with the actual population and employment as reported in the census. The results indicate that building floor space, together with the city level aggregate information of building morphology, the density coefficient, demographic attributes, and real estate statistics, are able to generate a reasonable estimation.

Developing Sustainable Urban Regeneration (Sur) Evaluation Method for the Malaysian Context

“…In terms of Environment Sustainability, Transportation emerged with the highest weight (0.65), followed by Resource Use (0.23), and Development Form (0.12) as shown in Table 6. Given the pivotal role of transportation in driving developmental growth, its impact on environment sustainability is significant (Teh et al, 2019). A clear cause-and-effect example lies in the construction of roads to accommodate development, often at the expanse of depleting existing natural resources.…”

Population estimation beyond counts—Inferring demographic characteristics

“…Geospatial real estate and housing stock data has been included in the analysis, since it has repeatedly proven to be significant in previous population estimation approaches [ 6 , 15 ], and has been extensively linked to demography in other studies [ 49 – 51 ]. Examples of housing predictors that have been used are the number of buildings, their footprint area, floor area, and volume [ 20 , 47 , 52 , 53 ]. Nowadays, real estate datasets are available from commercial websites or the government, and may support population estimation methods significantly [ 15 , 28 , 54 ].…”

Characteristics and influence factors exploration of conventional bus stop ridership in Beijing using multi-source data

“…Current studies on the influence factors of public transportation ridership have varied from built environment [1][2][3][4][5] , socioeconomics [6] , transportation modes [7] and policy [8] . Regarding the built environment, Kuby [1] et al analyzed ridership data for 268 light-rail stations in nine US cities, revealing the importance of land use, employment, and rental rate factor.…”

“…Regarding the built environment, Kuby [1] et al analyzed ridership data for 268 light-rail stations in nine US cities, revealing the importance of land use, employment, and rental rate factor. Teh [2] et al found that building floor space is useful for estimating station ridership. Zhao [3] et al studied the correlation between land use and the construction of metro station in Beijing, while Jun [4] et al found that land use diversity positively affects metro station ridership in Seoul.…”

“…For transportation modes, Cervero [7] found that increasing the proportion of higher sidewalks in residence and workplace can enhance public transportation ridership. For policy, Nasri [8] et al found that the probability of choosing transit and non-motorized modes is higher for trips originating and ending in TOD areas. In terms of data, the research is also expanding from traditional IC Card data and land use data [9] to more multi-source data such as POI data [10] .…”