Ko Ko Lwin

Modelling of urban green space walkability: Eco-friendly walk score calculator

Abstract Walkability is a well-known count of how conducive an area is to walking to and from chosen destinations. Calculation of a walk score is widely used in accessibility studies to determine the ease or difficulty of travel by foot between one point and another. The main purpose of this paper is to propose an integrated methodology (Remote Sensing, GIS and Spatial Web Technology) to model urban green space walkability, which enables local residents to make informed decisions that will improve their living conditions and physical health related to the neighbourhood environmental quality. We discuss the modelling of urban green space walkability by utilising web-based Geographical Information System (web-GIS) to calculate eco-friendly walk scores based on the presence of green spaces by integrating Advanced Land Observing Satellite (ALOS) data and other GIS datasets. We use this spatial web technology to help local residents make decisions related to neighbourhood environmental quality, such as how to choose an eco-friendly living space when buying a home or how to find the shortest or greenest route to walk to improve their health.

Web-based GIS System for Real-time Field Data Collection Using Personal Mobile Phone

Recently, use of mobile communicational devices in field data collection is increasing such as smart phones and cellular phones due to emergence of embedded Global Position System GPS and Wi-Fi Internet access. Accurate timely and handy field data collection is required for disaster management and emergency quick responses. In this article, we introduce web-based GIS system to collect the field data by personal mobile phone through Post Office Protocol POP3 mail server. The main objective of this work is to demonstrate real-time field data collection method to the students using their mobile phone to collect field data by timely and handy manners, either individual or group survey in local or global scale research.

Estimation of Building Population from LIDAR Derived Digital Volume Model

Advantages of dasymetric map over traditional choropleth map have been well documented in many cartographic journals. Dasymetric uses ancillary dataset to create smaller geographical unit of population. In fact, the smaller geographical unit of population data is required for effective disaster management, emergency preparedness, retail market competition, health and disease studies, crime analysis and other population data analysis at micro-scale level. In this chapter, we discuss new dasymetric mapping technique based on GIS estimated building population which was computed from building footprints, census tract and LIDAR derived Digital Volume Model DVM.

Evaluation of land cover classification based on multispectral versus pansharpened landsat ETM+ imagery

Land cover generated from satellite images is widely used in many real-world applications such as natural resource management, forest type mapping, hydrological modeling, crop monitoring, regional planning, transportation planning, public information services, and so on. Moreover, land cover data are one of the primary inputs to many geospatial models. In South-East Asias cities where the houses are interspersed with small trees, bare land and grassland are difficult to detect in multispectral Landsat ETM+ images because its 30 × 30 m spatial resolution is likely to capture a variety of land cover within each pixel, particularly in urban areas. Although other medium resolution multispectral satellites such as ALOS, SPOT, IRS, and so on have higher spatial resolution than Landsat ETM+, it is sometimes difficult to extract the built-up or human settlement areas because of the lack of shortwave infrared bands, which are very useful for distinguishing between soil and vegetation. In this article, we generated land cover data from both Landsat ETM+ multispectral and pansharpened images by applying the same training areas but using different spectral properties. We differentiated between two classified images visually, spectrally, and spatially. Our results showed that 65% of the total area had similar land cover and 35% had dissimilar land cover. Although dense urban areas, forest, agricultural land, and water were almost the same in the classified images, sparse urban areas and grassland were quite different. Much of the sparse urban areas were detected using the pansharpened classified imagery. This is important in South-East Asian cities where many houses are mixed with trees or grassland. Accurate delineation of human settlement area plays a critical role in population estimation, socio-economic studies, disaster management, and regional development planning.

Space–time multiple regression model for grid-based population estimation in urban areas

ABSTRACT We can collect, store, and analyze a huge amount of information about human mobility and social interaction activities due to the emergence of information and communication technologies and location-enabled mobile devices under cyber physical system frameworks. The high spatial resolution of population data on a multi-temporal scale is required by transport planners, human geographers, social scientists, and emergency management teams. In this study, we build a space-time multiple regression model to estimate grid-based (500 m × 500 m) spatial resolution at multi-temporal scale (30-min intervals) population data based on the space-time relationship among geospatially enabled person trip (PT) survey data and incorporate both mobile call (MC) and geotagged Twitter (GT) data. Since using geospatially enabled PT survey data as dependent variables enables us to acquire actual population amounts, which strongly depend on MCs and social interaction activities. Although many grids have a strong correlation between PT and MC/GT, some show fewer correlation results, especially where the grids have factories, schools, and workshops in which fewer MCs are found but a large population is presented. Although GT data are sparser than MCs, people from amusement and tourist areas can be detected by GT data. The space-time multiple regression model can also estimate the different amounts of populations based on human travel behavior that changes over space and time. According to accuracy assessments, the night-time estimated results, especially between 00:00 and 06:30, strongly correlate with national census data except in places where the grids have railway and subway stations.

Geovisualization and correlation analysis between geotagged Twitter and JMA rainfall data: Case of heavy rain disaster in Hiroshima

With the development of wireless communication technology along with emergence of location-enabled mobile devices and cyber-physical social sensor networks, nowadays we can collect and store a large amount of geospatial data such as weather phenomena, human mobility, and social networking activities along with time of occurrence. Information or knowledge extraction from this so-called Big Data is a challenge to many geospatial information users due to the nature of data complexity and large data volume. In this article, we discuss handling of Big Data with GIS by utilizing 10-minute intervals of Japan Metrological Agency (JMA) rainfall data synchronized with Twitter messages by specific keywords like “rain” or/and “landslide” to analyse the relationship between environmental phenomena and social responses in heavy rain conditions in the Hiroshima region. The results were analysed and visualized through a geovisualization technique to evaluate the possible use of inputs from social media websites to the government decision-making process, especially for disaster and emergency preparedness in the near future.

Person trip data browser, analyzer and space-time visualizer

Understanding human mobility from a spatial perspective is important to urban transport planners, human geographers, social scientists and other geospatial information users in order to improve public transport planning, sustainable urban planning and socio-economic planning by acquiring information about transportation mode by age, gender, occupation, purposes, etc. This PTS Person Trip Survey data was converted as one minute synthesized dataset by applying OD Origin and Destination route identification process and stored in csv files. However, handling of these large amounts PTS data requires high-speed computational power, complex database management systems and considerable period of time to extract, manipulate and analyse the data for end users. In this paper we discuss about handling of large scale PTS data with Web-GIS in order to use in scientific and research communities in time and cost effective way.

Data Collection, Processing, and Applications for Geospatial Analysis

Geospatial data collection is an important task for many spatial information users. Geospatial data collection may include field data collection, remote sensing data processing, and in-house geographical information science (GIS) data conversion. Nowadays, geospatial data are available from various sources. Among these, remote sensing data (i.e., optical, radio detection and ranging (RADAR), light detection and ranging (LIDAR), etc.) are among the primary data sources in many GIS analyses. For example, high-resolution satellite images such as QuickBird, IKONOS, and aerial photographs are the basis for the generation of qualitative land-use maps (i.e., land-use zoning maps) and the delineation of transportation networks. Medium-resolution satellite images such as ALOS, SPOT, and Landsat TM/ETM are used in the generation of quantitative land-use maps (i.e., land cover maps) for regional-scale studies of changes in land use. The shuttle radar topography mission (SRTM) and LIDAR provide topographical characteristics for GIS analysis. Moreover, remote sensing data are important for environmental studies such as deforestation, global warming, and natural resource management. This technology captures the real-world information with various sophisticated sensors and platforms. However, building a GIS database is required for further geospatial analysis and mapping purposes. GIS converts the real-world information into a geodatabase in order to retrieve, analyze, and allow further geocomputations. On the other hand, field data collection is important for spatial information users in order to collect spatially distributed objects with their associated attribute information. In this chapter, we discuss geospatial data collection methods and processing, and their applications in GIS.

GIS Network Model in Geospatial Analysis

Geographic information systems (GIS) provide both theory and methods that have the potential to facilitate the development of spatial analytical functions and various GIS data models. There are several network models in GIS, such as river networks, utility networks and transportation or road networks. Among these, GIS road network data models are important for solving problems in urban areas such as transportation planning, retail market analysis, accessibility measurements, service allocation and more. Understanding the road network patterns in urban areas is important for human mobility studies, because people are living and moving along the road networks. A network data model allows us to solve daily problems such as finding the shortest path between two locations, looking for the closest facilities within a specific distance or estimating drive times. Although many network models are conceptually simple they are mathematically complex and require computational resources to model the problem.

Web-Based Interactive Walkability Measurement Using Remote Sensing and Geographical Information Systems

The concept of walkability conveys how conducive the built environment is to walking. It has been adopted in many parts of the world to predict people’s physical activity and mode of transportation (Frank and Engelke 2005; Owen et al. 2004; Sallis et al. 2004). Walkability captures the proximity between functionally complementary land uses (live, work, and play) and the directness of a route or the connectivity between destinations (Forsyth and Southworth 2008; Moudon et al. 2006). A walk score is an indicator of how “friendly” an area is for walking. This score is related to the benefits to society in terms of energy savings and improvements in health that a particular environment offers to its residents. For example, a recently developed walk score web site uses Google Maps, specifically Google’s local search application programming interface (API), to find stores, restaurants, bars, parks, and other amenities within walking distance of any address entered. The walk score currently includes addresses in the United States, Canada, and the United Kingdom. The algorithm behind this score indicates the walkability of a given route based on the fixed distance from one’s home to nearby amenities. The number of amenities found nearby is the leading predictor of whether people will walk rather than take another travel mode. However, evaluating walkability is challenging because it requires the consideration of many subjective factors (Reid 2008). Moreover, all technical disciplines related to walkability have their own terminology and jargon (Abley 2005).