Point Density Analysis Research Articles

Could free-floating bikeshare weed out station-based bikeshare? Analyzing the relationship between two bikeshare systems from bivariate flow clustering

Against the backdrop of the strong market expansion of free-floating bikeshare systems (FFBS), the future of government-funded station-based bikeshare system (SBBS) is a matter of controversy. Merely relying on point density analysis proves to be inadequate in reflecting the flow characteristic, this paper employs a flow clustering analysis to investigate the relationship between SBBS and FFBS. To recognize both bikeshare flow clusters with inhomogeneous density and shape in less time, we propose a two-step network-constrained bivariate flow clustering method that organically combines multiplex-network community detection and bivariate flow clustering method. The performance and applicability of the method in flow clustering detection is exemplified by the SBBS and FFBS systems in Nanjing. The results indicate that though FFBS outnumbers SBBS in terms of bikes, there are still specific flow clusters where SBBS performs better. Spatiotemporal patterns of flow clusters reveal that about one-third of flow clusters dominated by FFBS move from the metro station to the business district during the morning peak, while more SBBS-dominated flow clusters (55.4%) move from the residence to the metro station. Conversely, during the evening peak, flow clusters are observed in the opposite direction. Nevertheless, if the difference in bike numbers is significant, SBBS will be at a disadvantage. It is necessary for SBBS to prioritize resource allocation towards target groups and advantageous areas to prevent decentralized development. Our findings contribute to a more profound comprehension of the interplay between SBBS and FFBS, thereby offering more informed recommendations for strategically aligning the functions of bikeshare systems.

GIS-based spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities

Malaria poses a significant threat to global health, with particular severity in Nigeria. Understanding key factors influencing health outcomes is crucial for addressing health disparities. Disease mapping plays a vital role in assessing the geographical distribution of diseases and has been instrumental in epidemiological research. By delving into the spatiotemporal dynamics of malaria trends, valuable insights can be gained into population dynamics, leading to more informed spatial management decisions. This study focused on examining the evolution of malaria in Nigeria over twenty years (2000–2020) and exploring the impact of environmental factors on this variation. A 5-year-period raster map was developed using malaria indicator survey data for Nigeria’s six geopolitical zones. Various spatial analysis techniques, such as point density, spatial autocorrelation, and hotspot analysis, were employed to analyze spatial patterns. Additionally, statistical methods, including Principal Component Analysis, Spearman correlation, and Ordinary Least Squares (OLS) regression, were used to investigate relationships between indicators and develop a predictive model. The study revealed regional variations in malaria prevalence over time, with the highest number of cases concentrated in northern Nigeria. The raster map illustrated a shift in the distribution of malaria cases over the five years. Environmental factors such as the Enhanced Vegetation Index, annual land surface temperature, and precipitation exhibited a strong positive association with malaria cases in the OLS model. Conversely, insecticide-treated bed net coverage and mean temperature negatively correlated with malaria cases in the same model. The findings from this research provide valuable insights into the spatiotemporal patterns of malaria in Nigeria and highlight the significant role of environmental drivers in influencing disease transmission. This scientific knowledge can inform policymakers and aid in developing targeted interventions to combat malaria effectively.

Re-examining the reused Roman masonry at Rubers Law hillfort

Antiquarian exploration of the archaeological features on Rubers Law (NGR: NT 58030 15567), a large hill in the Scottish Borders, found approximately 30 worked Roman stones on the summit of the hill. Based on the prominent location and visibility from the site, these stones led to the conclusion that there had been a Roman tower on the hill, despite a lack of further supporting evidence. In order to investigate the possibility of a Roman structure on the hill, two surface surveys of the stone tumble were conducted in 2016 and 2017, locating almost 300 sandstone blocks around the summit of Rubers Law. The distribution of the stones was analysed using point density analysis in GIS and was examined in relation to the remaining archaeological features on the summit. Based on the hotspots in the distribution of the stones, the majority were in a tertiary context after being reused in the construction of a post-Roman nucleated hillfort. In light of recording a much larger number of Roman stones on Rubers Law, this paper also reinterprets the evidence for a Roman tower and explores other possible structures that could have been built on the summit.

Territorial patterns of COVID-19 in Iran

Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

A COMPARATIVE STUDY OF THE BIVARIATE STATISTICAL METHODS AND THE ANALYTICAL HIERARCHICAL PROCESS FOR THE ASSESSMENT OF MASS MOVEMENT SUSCEPTIBILITY. A CASE STUDY: THE LM-116 ROAD – PERU

It has been long observed that the Peruvian Central Highway (PE-22) and the LM-116 road are among the roads most affected by mass movements (MM) in Peru, frequently exposed to the occurrence of rockfalls, debris flow and landslides; both roads represent an important connection alternative between Lima with towns, cities and mining centers located in the Central Mountain Range of the Andes. In this research, firstly, a point density analysis was performed using Geographic Information Systems (GIS) considering the road network of all of Peru (composed of 144,499 km) and the inventory of geological hazards (GEOCATMIN) prepared by the Geological, Mining and Metallurgical Institute of Peru INGEMMET (2000-2018). Subsequently, the evaluation of the mass movement susceptibility on the LM-116 road has been carried out using free access data reported by Peruvian institutions (INGEMMET, MTC, MINAM) from which it was possible to elaborate thematic maps, including the most relevant factors in the occurrence of mass movements, like a slope, lithology, geomorphology, land use, drainage density, and the distance from tectonic structures. Finally, for the mass movement susceptibility analysis, three methods have been considered: the Analytic Hierarchical Process (AHP), the Statistical Index (Wi) and the Weights of Evidence (WoE). The results were validated using the area under the curve criteria (AUC). Both bivariate statistical methods (Wi and WoE) presented a prediction rate above 78%, with a higher rate for the WoE method. On the other hand, the semi-quantitative method (AHP) obtained values in the order of 69%. Therefore, it is concluded that the maps elaborated with the statistical methods presented a better approximation concerning the database of geological hazards reported by GEOCATMIN.

Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters

In recent decades, the feasibility of using terrestrial laser scanning (TLS) in forest inventories was investigated as a replacement for time-consuming traditional field measurements. However, the optimal acquisition of point clouds requires the definition of the minimum point density, as well as the sensor positions within the plot. This paper analyzes the effect of (i) the number and distribution of scans, and (ii) the point density on the estimation of seven forest parameters: above-ground biomass, basal area, canopy base height, dominant height, stocking density, quadratic mean diameter, and stand density index. For this purpose, 31 combinations of TLS scan positions, from a single scan in the center of the plot to nine scans, were analyzed in 28 circular plots in a Mediterranean forest. Afterwards, multiple linear regression models using height metrics extracted from the TLS point clouds were generated for each combination. In order to study the influence of terrain slope on the estimation of forest parameters, the analysis was performed by using all the plots and by creating two categories of plots according to their terrain slope (slight or steep). Results indicate that the use of multiple scans improves the estimation of forest parameters compared to using a single one, although using more than three to five scans does not necessarily improves the accuracy. Moreover, it is also shown that lower accuracies are obtained in plots with steep slope. In addition, it was observed that each forest parameter has a strategic distribution depending on the field of view of the TLS. Regarding the point density analysis, the use of 1% to 0.1% (≈136 points·m−2) of the initial point cloud density (≈37,240.86 points·m−2) generates an R2adj difference of less than 0.01. These findings are useful for planning more efficient forest inventories, reducing acquisition and processing time as well as costs.

Extraction of Forestry Parameters Based on Multi-Platform LiDAR

Quick and accurate acquisition of tree height (TH) and diameter at breast height (DBH) plays a very important role in forestry surveys. These parameters can be collected rapidly and accurately with LiDAR. In this paper, an accurate tree parameters extraction method with combining the use of Unmanned Aerial Vehicle Laser Scanning (UAVLS) to extract TH and Terrestrial Laser Scanning (TLS) to extract DBH was proposed. To verify the applicability of this method, this paper collected LiDAR data in the Laohugou forest area (a natural forest) and Saihanba forest area (an artificial forest), Hebei Province, China. For the extraction of TH, both forest areas had overestimated. The coefficient of determination R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of TH in Laohugou forest area was 0.9458 and the root mean square error (RMSE) was 0.7 m, while in Saihanba forest area R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> was 0.95 and the RMSE was 0.65 m. A method based on point density analysis was proposed to automatically extract DBH. First, the data by TLS was normalized and made four-centimeter slices at 1.3 m. Then, branches, weeds and outliers were eliminated using an improved K-means algorithm. Finally, point density analysis was performed on all sections, and threshold values were set to automatically complete the extraction of DBH. The automatic DBH extraction by this paper proposed method was consistent with the actual measurements, and the mean intersection over union (MIOU) reached 89%. The R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of DBH in the Laohugou forest area was 0.9941 and the RMSE was 0.65 cm; the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of DBH in the Saihanba forest area was 0.99 and the RMSE was 0.43 cm. These results confirm that the accurate extraction of DBH in two forest areas with different growth conditions and different tree species.

Spatial Accessibility Patterns to Public Hospitals in Shanghai: An Improved Gravity Model

The spatial distribution of public hospitals is a crucial factor in patient access and thus important to health equity, especially amid the COVID-19 pandemic crisis. In China, a major issue is to improve accessibility to public health care in the face of the limited space for further growth of cities. Therefore, this study assesses the current spatial accessibility patterns to public hospitals in Shanghai, using point density analysis and the comprehensively improved gravity model. The results identify the challenges of inadequate and imbalanced spatial distribution of public hospitals at the district level and show that the central urban area has more high-quality public health care resources and also has higher accessibility levels. Based on the comparisons in each district, the study also summarizes four policy implications for future public hospital and public health care system optimization in Shanghai, which can also be leveraged for the planning of public hospitals in other cities or other public service facilities. The first is to deploy public hospitals in areas with a large population and to introduce branches of top-level hospitals to noncentral areas. The second is the full utilization of community health service centers to relieve the burden of public hospitals to improve accessibility.

Preferred hierarchical control strategy of phosphorus from non-point source pollution at regional scale.

Spatiotemporal heterogeneity poses challenges on prevention and control of non-point source (NPS) pollution. Treating pollution sources sequentially by prioritizing the critical periods (CPs) and critical source areas (CSAs) is essential for effective control of regional NPS pollution. In this study, the gird-based dual-structure export empirical model (DSEEM) was used to simulate phosphorus losses in the Danjiangkou Reservoir Basin (DRB) on a monthly scale. Based on the co-analysis of CPs and CSAs coupled with the point density analysis (PDA), a preferred hierarchical control strategy, which was connected with regional management units, was proposed to improve the pertinence for phosphorus loss control. CPs, sub-CPs, and non-CPs were identified on the temporal scale; CSAs, sub-CSAs, and non-CSAs were identified on the spatial scale. The results showed that CPs (July, April, and September), sub-CPs (May, March, and August), and non-CPs contributed 62.8%, 31.1%, and 6.1% of the annual TP loads, respectively. Furthermore, we proposed a hierarchical control strategy for NPS pollution: class I (CSAs in CPs) → class II (sub-CSAs in CPs, CSAs in sub-CPs) → class III (non-CPs, non-CSAs, sub- and non-CSAs in sub-CPs). Class I covered the periods and areas with the highest loads, contributing 26.2% of the annual loads within 14.5% of the area and 25.0% of the time. This study provides a reference for the targeted control of NPS pollution at regional scale, especially in environmental protection with limited funds.

Spatial Pattern and Hotspots of Urban Rail Public Transport to Public Access Using Geospatial Techniques in Selangor, Malaysia

Currently, the trends in urban public transport have been changing over the years in developing countries for mobilization and accessibility development. Urban public transportation systems are the most popular in Selangor State, including big cities such as the Klang Valley Region. Objective measures of spatial pattern and hotspots have been used to understand how urban public transport development relate to open access. This method relies on specific spatial information and available web-based tool that shows the pattern primarily based on given vicinity and statistics connectivity. To date, several studies have finished tested in developed countries. In this study, we use Geographic Information Systems to analyse and consider hotspots identification precisely and efficaciously. Therefore, in this paper, we focus on two types of point sample evaluations – Gi* hot spot and point density analysis evaluation as statistical operations. Public rail transport was evaluated as a validation to describe the percentage of distribution of open access. The final result, GIS mapping capabilities to show that GIS's technology offers to the variation of urban public transport relate to public services, is to create maps and spatial interpretations.

Spatial analysis of risk areas for the development of tuberculosis and treatment outcomes.

to identify risk clusters for the occurrence of tuberculosis and its treatment outcomes. ecological study, in a city in Maranhão, using data from the Notifiable Diseases Information System. Point density analysis and isotonic scanning techniques were used to identify areas with the highest occurrence of treatment outcomes and identify risk areas for possible tuberculosis cases. most tuberculosis cases occurred in the male, adult, brown-skinned population. Also, most of the reported cases were classified as pulmonary and as new cases that progressed to a cure. The areas with the highest density of cure, death and abandonment are located in the central region of the city. the central region of the urban area of the city, with high demographic density and poor sanitary and socioeconomic conditions, presented a greater cluster of tuberculosis cases.

An estimation of shipping emissions to analysing air pollution density in the Izmir Bay

The city of Izmir is one of the most polluted provinces in Turkey. The purpose of the research is to identify the risky areas in terms of air pollution using the emission density distribution data from ships called at Port of Alsancak in 2018. A geographic information system (GIS)–based emission inventory model was used to examine spatial changes in emissions in the Izmir Bay. Initially, by the bottom-up method, 187 movements from 41 ships combined have been evaluated. A spatial distribution map of air pollutants was then compiled using the point density analysis in GIS. The analysis revealed that SO2, NOX, CO2, PM10 and HC emissions from ships were identified as 334.5 t, 398.9 t, 19653 t, 37.4 t and 27 t, respectively. Besides, the research revealed that container ships accounted for the largest share (~ 91.3%) of all emissions. In conclusion, the highest emissions were recorded in Port of Alsancak and inner bay due to long hoteling periods. The port area was determined as the riskiest region that threatens public health. The study recommends emission reduction strategies such as replacement of ships’ source of energy in ports (e.g. cold ironing) and increase usage of green fuels.

Mapping the spatial distribution of emissions from domestic shipping in Izmir Bay

The purpose of this study is to determine the risky areas in terms of air pollution from domestic shipping in Izmir Bay with the integration of Geographic Information System (GIS) and emission evaluation methodology. To do this, initially, the emissions as a result of 435 trips of 19 ships engaged in domestic transportation in Izmir Bay were evaluated by the bottom-up method. The spatial distribution map was then compiled using the point density analysis through MapInfo v.16 software as a GIS instrument. The study revealed that annual emissions of PM10,CO2,NOx, SO2, and HC from domestic shipping were 601.5 t, 925.9 t, 62612.1 t, 22.0 t, and 61.2 t, respectively. In addition, the highest emissions as ton per daily trip were recorded in the cruising mode due to longer operation periods compared to the other modes. It was found that the lines between Pasaport, Alsancak and Karsiyaka piers are the most polluted routes due to dense ship traffic. Furthermore, Bostanli and Karsiyaka piers were determined as the points with the lowest air quality due to heavy traffic. We concluded that the ship emissions do not spread homogeneously throughout the region. In order to reduce the emissions in the area, the number of daily voyages and ship size should be optimized for passenger and vehicle density. The study also recommends installing emission monitoring stations at the piers to control air pollution.

Recovering Missing Trajectory Data for Mobile Laser Scanning Systems

Trajectory data are often used as important auxiliary information in preprocessing and extracting the target from mobile laser scanning data. However, the trajectory data stored independently may be lost and destroyed for various reasons, making the data unavailable for the relevant models. This study proposes recovering the trajectory of the scanner from point cloud data following the scanning principles of a rotating mirror. Two approaches are proposed from different input conditions: Ordered three-dimensional coordinates of point cloud data, with and without acquisition time. We recovered the scanner’s ground track through road point density analysis and restored the position of the center of emission of the laser based on plane reconstruction on a single scanning line. The validity and reliability of the proposed approaches were verified in the four typical urban, rural, winding, and viaduct road environments using two systems from different manufacturers. The result deviations of the ground track and scanner trajectory from their actual position were a few centimeters and less than 1 decimeter, respectively. Such an error is sufficiently small for the trajectory data to be used in the relevant algorithms.

Geographic differences in the distribution of manufacturing firms in Ontario, Canada

For at least the past 20 years, the Province of Ontario – as well as other advanced economic regions – has experienced a period of transition, with traditional manufacturing declining and advanced manufacturing firms and sectors emerging. Concurrently, there has been a concerted policy effort emphasising the development of industry clusters within the province, though key issues of governance and where and how to invest public funds remain. This paper presents research on the spatial configuration of firms in 26 manufacturing sub‐sectors, using point density analysis of the basis for determining the presence and spatial configuration of firms. This allows for the comparison of patterns observed in traditional, advanced, and evolving sub‐sectors. The findings show that there are patterns of clustering in all sub‐sectors, though different spatial configurations are apparent between traditional, advanced, and evolving sub‐sectors. These differences have implications for both investment and governance. Based on the findings, investment needs to be directed towards areas with locational advantages, while regional perspectives and initiatives on cluster governance are needed.

Addressing the data guardian and geospatial scientist collaborator dilemma: how to share health records for spatial analysis while maintaining patient confidentiality

BackgroundThe utility of being able to spatially analyze health care data in near-real time is a growing need. However, this potential is often limited by the level of in-house geospatial expertise. One solution is to form collaborative partnerships between the health and geoscience sectors. A challenge in achieving this is how to share data outside of a host institution’s protection protocols without violating patient confidentiality, and while still maintaining locational geographic integrity. Geomasking techniques have been previously championed as a solution, though these still largely remain an unavailable option to institutions with limited geospatial expertise. This paper elaborates on the design, implementation, and testing of a new geomasking tool Privy, which is designed to be a simple yet efficient mechanism for health practitioners to share health data with geospatial scientists while maintaining an acceptable level of confidentiality. The basic premise of Privy is to move the important coordinates to a different geography, perform the analysis, and then return the resulting hotspot outputs to the original landscape.ResultsWe show that by transporting coordinates through a combination of random translations and rotations, Privy is able to preserve location connectivity among spatial point data. Our experiments with typical analytical scenarios including spatial point pattern analysis and density analysis shows that, along with protecting spatial privacy, Privy maintains the spatial integrity of data which reduces information loss created due to data augmentation.ConclusionThe results from this study suggests that along with developing new mathematical techniques to augment geospatial health data for preserving confidentiality, simple yet efficient software solutions can be developed to enable collaborative research among custodians of medical and health data records and GIS experts. We have achieved this by developing Privy, a tool which is already being used in real-world situations to address the spatial confidentiality dilemma.

Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data

Abstract. Over the last years, we have witnessed an increasing interest in urban health research using physiological sensors. There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, most of the studies focus mainly on the analysis of the physiological signals and disregard the spatial analysis of the extracted geo-located emotions. Methodologically, the use of hotspot maps created through point density analysis dominates in previous studies, but this method may lead to inaccurate or misleading detection of high-intensity stress clusters. This paper proposes a methodology for the spatial analysis of moments of stress (MOS). In a first step, MOS are identified through a rule-based algorithm analysing galvanic skin response and skin temperature measured by low-cost wearable physiological sensors. For the spatial analysis, we introduce a MOS ratio for the geo-located detected MOS. This ratio normalises the detected MOS in nearby areas over all the available records for the area. Then, the MOS ratio is fed into a hot spot analysis to identify hot and cold spots. To validate our methodology, we carried out two real-world field studies to evaluate the accuracy of our approach. We show that the proposed approach is able to identify spatial patterns in urban areas that correspond to self-reported stress.

Earthquake population loss estimation using spatial modelling and survey data: The Bam earthquake, 2003, Iran

This paper contributes to ongoing global efforts to estimate probable earthquake population losses. Several models have been developed to estimate the population losses such as the number of killed, injured and hospitalized, injured and non-hospitalized, trapped, and non-injured people. However, there was lack of research on the estimation of the number of trapped people in Iranian context which is the main reason in conducting of this study. Data were collected based on a two-stage cluster sampling method through an interview survey from 396 people who experienced the Bam earthquake in Iran, 2003 regarding building damage and population losses in each household. The cross-tabulation analysis was performed to calculate the probability of each class of population losses, and point density analysis in GIS was applied to identify the spatial distribution of population losses throughout the study area. Moreover, a Wilcoxon Signed Rank Test, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Fuzzy Inference System analyses were carried out to measure the validity of the statistical and spatial results in comparison with the Actual Earthquake (AE) data. These analyses clarified that survey data significantly improved estimation results of population losses against KHM model. Furthermore, the estimation of “the number of trapped people” according to data gathered in Iran is promising, as it can play a significant role in disaster response operations. This factor can make a key contribution to the efficiency of allocation of limited resources, particularly Urban Search and Rescue (USAR) operational teams.

Exploring Accommodation Cluster and Tourism Development Based on GPS Positioning and GIS Analysis

This research aims to locate a set of points of B&amp;B accommodations on a coffee cultivation area by using GPS positioning and GIS spatial analysis for responding to Porter's concept of industry cluster and to analyze the locational factors of B&amp;B accommodation. Due to the coffee attractiveness, the B&amp;B lodging establishments have sprung up in the area like mushrooms to service more and more tourists for the past 20 years. The spatial cluster of B&amp;B establishments is displayed by specifying a set of GPS positioning points on different GIS raster surfaces to process point density analysis, buffer analysis and terrain analysis. To explore the locational characteristic of B&amp;B establishments, overlay analysis is processed to examine the terrain, landscape, accessibility and tourist attractiveness factors. Instead of focusing on innovation atmosphere, knowledge intensive and technology transfer of new industry cluster factors, this research emphasizes traditional cluster concept of geography proximity on the benefits of agriculture and tourism linkage to available of tourist foods.

Open spaces and risk perception in post-earthquake Kathmandu city

Perceptions of seismic risks, among other factors, are influenced by urban environments. This relationship is investigated in this paper, in relation to open spaces. A comparative study of two communities in Kathmandu, Nepal with the context of 2015 earthquake was conducted using data gathered from household surveys and expert interviews. Escape behaviour in relation to open spaces was examined by analysing the correlation with a risk perception index (RPI) which is a novel approach in seismic risk perception studies. Additionally, point density analysis of surveyed houses and visualization of escape routes and destination followed by the respondents offer insights into the spatial relationship with perceived risk. Furthermore, expert interviews were used to validate the findings and highlight the important relationship between perceived risks and open spaces. The findings suggest that open spaces are a key component of disaster response as they are safe locations and offer spaces for community that enables mutual coping among its members. As such it directly or indirectly affect people's perception of seismic risk. It was found that medium sized communal spaces are preferred within a distance of 200 m as immediate safe destinations. The choices for such spaces are dependent on the built environment of the site given by its layout, landmarks, building density and building height. The choices of open spaces as shelter locations are influenced by duration of stay such as availability of drinking water, public lavatory and electricity are crucial for short term stay where as ownership and economic capabilities are vital for long term stay.