This paper attempts to ascertain the impacts of population density on the spread and severity of COVID-19 in Malaysia. Besides describing the spatio-temporal contagion risk of the virus, ultimately, it seeks to test the hypothesis that higher population density results in exacerbated COVID-19 virulence in the community. The population density of 143 districts in Malaysia, as per data from Malaysia’s 2010 population census, was plotted against cumulative COVID-19 cases and infection rates of COVID-19 cases, which were obtained from Malaysia’s Ministry of Health official website. The data of these three variables were collected between 19 January 2020 and 31 December 2020. Based on the observations, districts that have high population densities and are highly inter-connected with neighbouring districts, whether geographically, socio-economically, or infrastructurally, tend to experience spikes in COVID-19 cases within weeks of each other. Using a parametric approach of the Pearson correlation, population density was found to have a moderately strong relationship to cumulative COVID-19 cases (p-value of 0.000 and R² of 0.415) and a weak relationship to COVID-19 infection rates (p-value of 0.005 and R² of 0.047). Consequently, we provide several non-pharmaceutical lessons, including urban planning strategies, as passive containment measures that may better support disease interventions against future contagious diseases.

With the popularization of smart mobile terminals and social media, a large amount of data containing textual information about the city has been generated on social media platforms, covering all areas of the city. This provides a new way for the study of comprehensive perception of city image. In the Internet era, users express their opinions about cities through social media platforms (e.g., Sina Weibo), and mining this information helps to understand the image of cities on mainstream social media and to target positive images to improve the competitiveness of the city's image. In this paper, 370,000 microblog messages related to "Guangzhou City" between 2019 and 2023 are collected using web crawler technology, and three typical text analysis methods are adopted: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Sentiment Analysis (SnowNLP), to understand the characteristics of Guangzhou city image. gain an in-depth understanding of Guangzhou's urban image characteristics. The study shows that extensive data analysis methods based on text mining can perceive the dynamics and trends of the city in a timely manner, refine the characteristics of Guangzhou's urban image, and propose communication strategies for Guangzhou's image. This study aims to mine Guangzhou's urban image presented on Weibo, provide data support for relevant departments in China and Guangzhou to formulate communication strategies, and provide references for other cities to manage their urban image.

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.

This paper attempts to examine the factors affecting the COVID-19 pandemic situation in Malaysia. It investigates three major factors (social, economy and environment). Thirteen States and two Federal Territories of Malaysia were considered; and the data for the attributes of each major factor are derived from the official reports from the Department of Statistics Malaysia. Meanwhile, the infection rate and mortality rate of COVID-19 cases were obtained from the Ministry of Health, Malaysia. Using non-parametric statistical approach, the several interesting results are identified. Firstly, for the social factor, we found that the percentage of non-citizens has a positive relationship with both COVID-19 infection rate and mortality rate. Further, the number of students per teacher have a positive relationship with COVID-19 infection rate. Second, in terms of the economy factor, primary industry has a negative relationship with COVID-19 infection rate. Third, in the matter of social factor, it is found that population density and percentage of high-rise residential unit are positively related with COVID-19 infection rate. The result from this study can provides an insight for policymakers to understand factors contribute on the spread and severity of COVID-19 to informing better mitigation policy and control measures.

In this study, we explore how to balance the preservation of the original appearance of ancient villages with their development within the framework of World Heritage protection. We applied resilience theory and constructed a simple checklist, taking cultural ecosystem services into consideration, and selected the Zuojiang Huashan Rock Art Heritage Area in China for field investigation, as well as conducted in-depth interviews, the distribution of short questionnaires, and two rounds of Delphi surveys. This comprehensive approach enabled us to discover the key cultural ecosystem services that villagers rely on for their livelihoods. Then, we tracked how these services enhanced buffering capacity, helped people self-organize, and promoted adaptive learning. The results show that cultural ecosystem services constitute the core framework of the social–ecological resilience of the villages. The quantity and combination of the services directly determine the resilience score, and the resilience of villages within the heritage area shows significant spatial differentiation. High-resilience villages have diverse and mutually reinforcing cultural ecosystem services and local community rules, while low-resilience villages face service loss, weakened social connections, and single development options. Through this study, we aim to further enrich the cultural connotation of resilience theory, provide a practical assessment tool for practitioners of the method, and offer practical guidance and suggestions for transforming heritage protection from static protection to a dynamic, vibrant system that promotes vitality and resilience in practice.

The move towards a more sustainable transport mode is crucial in the Klang Valley by incorporating feeder services to rail network stations. Feeder services will complement the expanding rail transits network and deliver connectivity for areas not covered within rail stations. Past literatures found that feeder services were ineffective during rail breakdown as feeder service was not going or stopping to where it was intended to. Feeder service can be allocated more effectively if we have better idea of targeted service area. Urban planners now have access to a variety of urban analytics because of technological advancements and the rise of smart cities. Data generated are produced across numerous locations and at diverse time scales, providing a significant possibility for data mining to uncover insightful information when both space and time are considered. This study will attempt to assess the community’s accessibility to rail transit through feeder service in Klang Valley. In achieving this a conceptual framework is developed within the objectives of this study which are firstly, to identify built environment factors that influence ridership around transit stations. Secondly, to determine the spatial -temporal relationship between built environment and transit ridership of feeder service and rail transits. Next, the station-level spatial and temporal variability of ridership data between feeder service to rail network is examined. Finally, as the main finding of this study, a framework in maximizing a service catchment transportation service for feeder service and rail transit will be developed. In doing that, the conceptual framework approach is by addressing numerical analytical technique in geographic data, by using spatial temporal regression. This study aspires to provide planning departments and transit agencies with useful guidance to execute targeted policies and develop a sustainable and efficient feeder service coverage area to support rail transits by looking at both spatial and temporal scales.