International Journal of Advances in Intelligent Informatics ISSN: 2442-6571 Vol. 3, No. 2, July 2017, pp. 98-106 98 DOI: http://dx.doi.org/10.26555/ijain.v3i2.93 W : http://ijain.org | E : info@ijain.org Spatial Data Modeling in Disposable Income Per Capita in China using Nationwide Spatial Autoregressive (SAR) Tuti Purwaningsih a,1,* , Anusua Ghosh b,1 , Chumairoh Chumairoh a,2 a Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Islam Indonesia, Indonesia b University of South Australia, Australia 1 tuti.purwaningsih@uii.ac.id *; 2 anusua.ghosh@mymail.unisa.edu.au; 3 chumairohazzahra@yahoo.com * corresponding author I. Introduction China is a country with an advanced economy, includes in the list of countries which has the biggest export, and China’s economic strength is predicted that will defeat the United States [1]. In 2014, the regional director for GBTA (Global Business Travel Association) reported that China's economic growth also encouraged the tourist’s business sector. Moreover, in the same year, China had made progress (economic) super fast with the value of GDP for 2014 was 28.3-fold rise and per capita rise 19-fold. Revitalization of the Chinese nation to make China's large emerging economies at the center of both worlds. However, with a population of 1.3 billion, China's per capita income is still at number 80 in the world, where 100 million people are still poor and are not in balance between town and country. With the advancement of a famous industry rapid development, China's per capita income was still not balanced between urban and rural areas; It is necessary assessments rely more deeply to solve it because after income per capita is often used as a benchmark for the prosperity of a country. If income per capita is greatest, the country will be judged increasingly affluent. Moreover, China belongs to the part of developed countries [1]. The understanding of factors that influence the per capita income is certainly very important so that It might be used as reference in decision-making in determining which factors greatly contribute to greater per capita income. It can be used as a strong basis in determining a policy that will be taken and is expected to facilitate the making of a policy so that the various possibilities that may occur regarding loss or weakness can be overcome. Also, tourism industry has a close relationship in advancing the economy in China. Therefore, the researchers aimed to examine how the influence of ARTICLE INFO ABSTRACT Article history: Received July 22, 2017 Revised August 21, 2017 Accepted August 21, 2017 China as a country became the economic center of the world. However, with a population of 1.3 billion, China's per capita income is still at number 80 in the world. In the world, considering the imbalance between town and country with 100 million people still living in poverty. Thus, to address this imbalance, it is necessary to study the condition in depth, because income per capita is often used as a benchmark to measure the prosperity of a country. With greater and equitable income per capita, the country will be judged increasingly affluent. Two factors, mainly industry and tourism, play an important role in the economic progress in China. These are include Per capita Disposable Income Nationwide (yuan), Total Value of Exports of operating units (1,000 USD), Registered Unemployed Person in Urban Area (10000 person), Foreign Exchange Earning from International tourism(in millions USD) and Number of Overseas Visitor Arrivals (million person/time). Thus, it is necessary to investigate the influence of these factors to increase per capita income. Since the economic development of a region usually affect the surrounding area, this study aims to include spatial effects, using Spatial Autoregressive (SAR) Model. The results suggest that the per capita income affected by the Tourism factor is about 58.65% (R- squared). Copyright © 2017 International Journal of Advances in Intelligent Informatics. All rights reserved. Keywords: Per Capita Disposable Income Nationwide Industry Tourism Spatial Autoregressive Model (SAR)