Spot Mapping Research Articles

How to Support Better Decision Making for Sustainable Development?

Sustainable development paradigm refers to three basic pillars (social, environmental and economic) which strongly interact between themselves. Therefore, solving some problems needs actions in different fields, as well as solving one problem may influence different aspects of life. One of the multidimensional elements of global system is the unemployment issue and actions which are undertaken to reduce inequalities in that field. However, the open questions are: how effective those actions are?, which elements current mechanisms need an improvement? and which methods can help to overcome current problems and suggest better decision making in the future? The aim of the research is to determine whether EU funds for employment incentive actions were concentrated in those places where the unemployment rate was the highest and whether social inequalities were reduced. The case study was the evaluation of over 6000 employment incentive projects carried out in 2008-2015 in biggest EU Member State in the Central Europe – Poland. The study was carried out for NUTS-4 units. In order to assess the concentration of geospatial variables the hot spot analysis was used. The results show that the support was not concentrated in those part of the country where the unemployment rate was the highest. There is still a place to improve the implementation of national policies to obtain more satisfying effects in social inequalities mitigation. The proposed method of hot spot mapping is suggested by authors as an element of decision support system, which may have a positive impact on effectiveness of institutional actions to reach sustainable development goals.

Magnetic field topologies of the bright, weak-field Ap stars θ Aurigae andεUrsae Majoris

Context.The brightest magnetic chemically peculiar stars θ Aur andεUMa were targeted by numerous studies of their photometric and spectroscopic variability. Detailed maps of chemical abundance spots were repeatedly derived for both stars. However, owing to the weakness of their surface magnetic fields, very little information on the magnetic field geometries of these stars is available.Aims.In this study we aim to determine detailed magnetic field topologies of θ Aur andεUMa based on modern, high-resolution spectropolarimetric observations.Methods.Both targets were observed in all four Stokes parameters using the Narval and ESPaDOnS spectropolarimeters. A multi-line technique of least-squares deconvolution was employed to detect polarisation signatures in spectral lines. These signatures were modelled with a Zeeman-Doppler imaging code.Results.We succeed in detecting variable circular and linear polarisation signatures for θ Aur. Only circular polarisation was detected forεUMa. We obtain new sets of high-precision longitudinal magnetic field measurements using mean circular polarisation metal line profiles as well as hydrogen line cores, which are consistent with historical data. Magnetic inversions revealed distorted dipolar geometries in both stars. The Fe and Cr abundance distributions, reconstructed simultaneously with magnetic mapping, do not show a clear correlation with the local magnetic field properties, with the exception of a relative element underabundance in the horizontal field regions along the magnetic equators.Conclusions.Our study provides the first ever detailed surface magnetic field maps for broad-line, weak-field chemically peculiar stars, showing that their field topologies are qualitatively similar to those found in stronger field stars. The Fe and Cr chemical abundance maps reconstructed for θ Aur andεUMa are at odds with the predictions of current theoretical atomic diffusion calculations.

STRATEGY FOR EXTRACTION OF FOURSQUARE’S SOCIAL MEDIA GEOGRAPHIC INFORMATION THROUGH DATA MINING

Abstract This aim of this paper is the acquisition of geographic data from the Foursquare application, using data mining to perform exploratory and spatial analyses of the distribution of tourist attraction and their density distribution in Rio de Janeiro city. Thus, in accordance with the Extraction, Transformation, and Load methodology, three research algorithms were developed using a tree hierarchical structure to collect information for the categories of Museums, Monuments and Landmarks, Historic Sites, Scenic Lookouts, and Trails, in the foursquare database. Quantitative analysis was performed of check-ins per neighborhood of Rio de Janeiro city, and kernel density (hot spot) maps were generated The results presented in this paper show the need for the data filtering process - less than 50% of the mined data were used, and a large part of the density of the Museums, Historic Sites, and Monuments and Landmarks categories is in the center of the city; while the Scenic Lookouts and Trails categories predominate in the south zone. This kind of analysis was shown to be a tool to support the city's tourist management in relation to the spatial localization of these categories, the tourists’ evaluations of the places, and the frequency of the target public.

Outbreak investigation and containment measures of dengue fever in rural Wardha: A field epidemiological study

Background: Dengue is a common vector-borne disease in countries like India, where it occurs in specific pockets including many in Maharashtra. During July–August 2018, an outbreak of dengue was suspected in a village of Wardha district, following an increase in the number of fever cases from the village, which demanded an urgent outbreak investigation to be carried out before it progressed into an epidemic/larger outbreak. Objectives: The aim of this study was to find out more cases, source of infection and to recommend necessary actions to control the dengue outbreak. Materials and Methods: This was a cross-sectional epidemiological study, where we used pre-tested, pre-designed epidemiological case sheets obtained from Integrated Disease Surveillance Program (IDSP). A case definition was proposed before beginning the investigation. Line listing, collection of blood samples, distribution of paracetamol tablets to fever cases, and making a spot map were some of the important steps carried out during the investigation. Results: Out of a total of 64 suspected fever cases, 36 cases fulfilled the case definition of dengue, set for the purpose of outbreak investigation. Twenty-three of them got admitted in different hospitals among whom two of them died. Apart from that, four of them were found to be positive for both dengue IgM and NS1 antigen in their blood samples. It was age group of 21–30 years which was mostly affected. Compared to males, the female population suffered more from the illness. House, container, and Breteau index were found to be 0.26, 0.09, and 0.32, respectively. Conclusion: Following the investigation, a detailed report was made, which was shared with the district health authority. The report also contained recommendations for necessary actions to be taken to control this outbreak and to further prevent such outbreaks in future.

Relation between urban biophysical composition and dynamics of land surface temperature in the Kolkata metropolitan area: a GIS and statistical based analysis for sustainable planning

The major environmental impacts of urbanization have changed urban biophysical components which ultimately promoted land surface temperature (LST) as well as urban heat island (UHI). This study explores the upshot of land use land cover (LULC) and resultant effect on biophysical components to understand the heat island mechanism in the Kolkata Metropolitan Area (KMA) for four selected time period of 1991, 2001, 2011 and 2017. Six satellite-derived biophysical components were selected for the present analysis: NDBI, NDVI, NDWI, MNDWI, NDBaI and SAVI. Selected bands of Landsat-5 TM and OLI-8 were used for this purpose. The result shows that the built-up area has been increased from 322.68 km2 in 1991 to 982.86 km2 in 2017 and accordingly, LST also rises from 18.47 °C mean LST of 1991 to 23.30 °C mean LST of 2017. The correlation coefficient among the biophysical parameters and LST shows that the highest continuous increasing positive relationship between NDBI and LST (R = 0.71). Moreover, multiple linear regression model (MLR) is adapted to the prediction on LST with the variation of biophysical parameters. Finally, we produced hot spot maps using Getis-Ord-Gi* statistics for the selected year to highlights the hot spot and cold spot area in KMA. The methodology presented in this paper can be broadly applied for the planning purposes because LST monitoring is an important parameter of sustainable urban planning.

Parking Spot Estimation and Mapping Method for Mobile Robots

Self-driving vehicles rely on detailed semantic maps of the environment for operating. In this letter, we propose a method to autonomously generate such a semantic map enriched with knowledge of parking spot locations. Our method detects and uses parked vehicles in the surroundings to estimate parking lot topology and infer vacant parking spots via a graph-based approach. We show that our method works for parking lot structures in different environments, such as structured parking lots, unstructured/unmarked parking lots, and typical suburban environments. Using the proposed graph-based approach to infer the parking lot structure, we can extend the estimated parking spots by 57%, averaged over six different areas with ten trials each. We also show that the accuracy of our algorithm increases when combining multiple trials over multiple days. With ten trials combined, we managed to estimate the whole parking lot structure and detected all parking spots in four out of the six evaluated areas.

Same space – different perspectives: comparative analysis of geographic context through sketch maps and spatial video geonarratives

ABSTRACTThe importance of including a contextual underpinning to the spatial analysis of social data is gaining traction in the spatial science community. The challenge, though, is how to capture these data in a rigorous manner that is translational. One method that has shown promise in achieving this aim is the spatial video geonarrative (SVG), and in this paper we pose questions that advance the science of geonarratives through a case study of criminal ex-offenders. Eleven ex-offenders provided sketch maps and SVGs identifying high-crime areas of their community. Wordmapper software was used to map and classify the SVG content; its spatial filter extension was used for hot spot mapping with statistical significance tested using Monte Carlo simulations. Then, each subject’s sketch map and SVG were compared. Results reveal that SVGs consistently produce finer spatial-scale data and more locations of relevance than the sketch maps. SVGs also provide explanation of spatial-temporal processes and causal mechanisms linked to specific places, which are not evident in the sketch maps. SVG can be a rigorous translational method for collecting data on the geographic context of many phenomena. Therefore, this paper makes an important advance in understanding how environmentally immersive methods contribute to the understanding of geographic context.

Development of a 2D and 3D computational algorithm for discontinuity structural geometry identification by artificial intelligence based on image processing techniques

Block geometry is commonly the most important feature determining the behaviour of a rock mass and directly controls the structural instability in underground openings or surface cuttings. Various methods are used to estimate block geometry and to perform a block survey, and these are standardly divided into empirical-based methods (e.g. spot mapping, linear mapping, areal mapping) and computer-based methods (e.g. laser scanning, image processing, digital image mapping). Empirical approaches are associated with effective features as well as with a number of errors; however, the latter can be covered by artificial intelligence (AI) techniques. The combination of image processing and areal mapping have led to geometric block estimation in two-dimensional (2D) and three-dimensional (3D) spaces; these approaches can be widely used in stability analysis, dimension stone extraction, excavations, open pit mining design and the delineation of blasting patterns for dimension stone extraction. Therefore, the application of an approach that allows both the modelling and production of rock blocks based on their actual status with qualified accuracy and speed are both worthwhile and necessary. In this study, to estimate the shape and block dimension utilized, we used the algorithm based on the AI image processing technique for rock mass structural detection and for rock block definition in 2D and 3D space obtained with the Mathematica software package. The algorithm, by categorizing the discontinuities in two groups (opened and closed), which represents the main and the secondary discontinuities, can identify the emplacement and shape of rock blocks.

Athletes’ perceptions of within-field variability on natural turfgrass sports fields

Natural turfgrass sports field properties exhibit within-field variations due to foot traffic from play, field construction, management, and weather. Little is known about the influences these variations may have on athletes’ perceptions of field playability and injury risk. Information regarding athletes’ perceptions of within-field variability could be fundamental for identifying key surface properties important to athletes, which may also be useful for the progression and implementation of Precision Turfgrass Management on sports fields. A case study using mixed methods was conducted on a recreational-level turfgrass sports field to better understand athletes’ perceptions of within-field variability. Geo-referenced normalized difference vegetation index, surface hardness, and turfgrass shear strength data were obtained to create hot spot maps for identification of significant within-field variations. Walking interviews were conducted in situ with 25 male and female collegiate Club Sports rugby and ultimate frisbee athletes to develop knowledge about athletes’ perceptions of within-field variability. Field data, hot spot maps, and walking interview responses were triangulated to explore, compare, and validate findings. Athletes’ perceptions of within-field variability generally corresponded with measured surface properties. Athletes perceived within-field variations of turfgrass coverage and surface evenness to be most important. They expressed awareness of potential influences the variations could have, but not all athletes made behavior changes. Those who reported changing did so with regard to athletic maneuvers and/or strategy, primarily for safety or context of play. Spatial maps of surface properties that athletes identified could be used for Precision Turfgrass Management to potentially improve perceptions by mitigating within-field variability.

Definition of Strategies for Crime Prevention and Combat Using Fuzzy Clustering and Formal Concept Analysis

Public security has always been an important research topic. In this sense, machine learning algorithms have been used to extract knowledge from criminal databases, which usually maintain records in order to generate statistics. The automatic extraction of knowledge from such databases allows the improvement and planning of strategies to prevent and combat crimes. Accordingly, in this work different models related to public security are presented. Such models are based on clustering algorithms, on the analysis of formal concept techniques, and on the analysis of crime record data collected in the city of Mossoro, Brazil. The two types of models generated are: (i) concept lattices with crime patterns; (ii) criminal hot spot maps. We also produced a ranking of dangerousness for neighbourhoods of Mossoro. The Fuzzy K-Means clustering algorithm was used to obtain criminal hot spots, which indicate locations with high crime incidence. Formal concept analysis was used for extracting visual models describing patterns that characterize criminal activities. Such models have the form of conceptual lattices that provide graphical displays which can be used for defining strategies to combat and prevent crime. The models were first empirically evaluated and then analysed by public security experts, who provided positive feedback for their practical use. The advantages of the automatically generated models presented in this paper are many, including the short time to produce such models, the variety of different models that can be generated for specific regions and periods of days, months, or years, the graphical characteristic of such models that allow a fast analysis of them, as well as the use of large amounts of data, which are infeasible activities to be done by human experts.

A multi-factor GIS method to identify optimal geographic locations for electric vehicle (EV) charging stations

Abstract. Fuel-based transportation is one of the major contributors to poor air quality in the United States. Electric Vehicle (EV) is potentially the cleanest transportation technology to our environment. This research developed a spatial suitability model to identify optimal geographic locations for installing EV charging stations for travelling public. The model takes into account a variety of positive and negative factors to identify prime locations for installing EV charging stations in Wasatch Front, Utah, where automobile emission causes severe air pollution due to atmospheric inversion condition near the valley floor. A walkable factor grid was created to store index scores from input factor layers to determine prime locations. 27 input factors including land use, demographics, employment centers etc. were analyzed. Each factor layer was analyzed to produce a summary statistic table to determine the site suitability. Potential locations that exhibit high EV charging usage were identified and scored. A hot spot map was created to demonstrate high, moderate, and low suitability areas for installing EV charging stations. A spatially well distributed EV charging system was then developed, aiming to reduce “range anxiety” from traveling public. This spatial methodology addresses the complex problem of locating and establishing a robust EV charging station infrastructure for decision makers to build a clean transportation infrastructure, and eventually improve environment pollution.

Non-Destructive Chemical State Mapping Using Laboratory XANES and EXAFS

X-ray absorption spectroscopy (XAS) is a popular technique for determining chemical states, such as oxidation states by XANES (x-ray absorption near edge structure) or interatomic distances and coordination numbers by EXAFS (extended x-ray absorption fine structure). These techniques are widely utilized by electrochemical researchers in synchrotron facilities worldwide, addressing a range of studies: for example, XANES can help to probe the oxidation states of battery electrode materials at various stages in their operational lifetime, EXAFS can elucidate the dynamics of conversion reactions in operando, thus capturing transients that may otherwise be lost in ex situ studies, and XAS experiments encompassing both XANES and EXAFS have shown unique promise for examining the behavior of sulfur in electrodes for Li-S batteries. The popularity of these techniques in tandem with the 3D microstructural information from X-ray tomography (XCT) and the compositional information available through other types of spectroscopy (e.g., XRF, EDS, and Raman) has grown substantially over the past two decades, as more synchrotron beamlines have become available for these experiments and the true power of the techniques have been demonstrated. In spite of the excitement generated by the various XAS experiments documented in the literature and the growing number of synchrotron facilities worldwide, access to suitable instrumentation remains challenging. This is due largely to the general popularity of synchrotron radiation for studying a variety of materials across a range of disciplines, leading to high levels of competition for limited amounts of available beam time. Efforts have been made in the past to migrate the XAS techniques to laboratory instrumentation, but such efforts have been met with many challenges. Often the exposure times are very long, resulting in multi-day measurements for a single experiment, or the energy resolution is too crude to provide sufficient sensitivity for precise quantification. Thus, a need remains for expanded access to XAS techniques within the electrochemical energy storage and conversion community, which has, so far, not been adequately addressed. Here, we present a novel, compact, laboratory-based XAS system operating in the 4.5-12 keV energy range with a powerful range of capabilities. This instrument provides sub-eV energy resolution over a fairly large energy range, short data collection times (seconds to minutes), and high spatial resolutions (~10 micrometers) for small spot analysis or mapping. The performance of the system is achieved in part with a patented high brightness x-ray source, a novel system design, and the use of the latest x-ray crystal analyzers and spatially resolved detector. Through the unique capabilities of this laboratory instrumentation, many experiments traditionally reserved for the synchrotron can now be accomplished in the laboratory. In this presentation, we will briefly review the spectrum of studies where XAS can provide insight into battery electrode materials and proceed to demonstrate how this laboratory XAS setup can address both routine and more specialized needs for electrochemical characterization. We will also discuss the relationship of this technique to other laboratory approaches, providing some perspectives on both the present and future of non-destructive characterization.

Factors Associated with Psychiatric Morbidity and Psychosomatic Symptoms among Adolescent School Girls in Khartoum State-Sudan

Psychosomatic health of adolescent girls at crossroads of childhood and mature adulthood, may lead to various health problems in future. To measure the prevalence and identify factors associated with psychiatric morbidity and psychosomatic symptoms among adolescent secondary school girls in Khartoum. This ‘Study’ was conducted, in three secondary schools of Khartoum North. The Simple Random Sampling Technique was applied to select three schools from the spot map of Khartoum North for this study, and 491 girl students in the adolescent age group were selected from the completed updated list of students from the enrollment registers in these schools. according to the cut-off point (≥ 24 in the G.H.Q-28) findings showed that the prevalence of psychiatric morbidity (severe depression and anxiety) was 60.89% among the adolescent secondary school girls while the psychosomatic disorder was 34.62%. The psychosomatic disorder was found to be significant (P 0.05) relation between the fathers' level of education and the occurrence of psychosomatic however, the odds ratio of disease is 28% higher among the girls whose fathers were illiterate in comparison to the girls whose fathers were of high level of education. among adolescent school girls.

Large cholera outbreak in Brong Ahafo Region-Ghana, 2014

Introduction : a nationwide outbreak of Vibrio cholera occurred in Ghana in 2014. Accra, the nation’s capital, was the epicenter. The outbreak spread to the Brong Ahafo region (BAR) which is geographically located in the middle belt of the country. In this region a review of data collected during the outbreak was carried out and analyzed descriptively to determine the hot spots in order to make recommendations for effective response to future outbreaks. Methods : a review of patient’s records and line lists of cases of cholera reported in all hospitals during the period of the outbreak (July-December 2014) was conducted. Hospitals used IDSR (integrated disease surveillance and response system) standard case definitions to detect and report cases for management. The GPS coordinates of all districts and health facilities were collected and utilized in the construction of spot maps. We also obtained populations (denominators) from the BAR Health surveillance unit of the Ghana Health service. All the data thus collected was analyzed descriptively and expressed as frequencies and rates. Results : a total of 1,035 cases were reported, 550 (53.4%) were males. Their ages ranged from 1 to 95 years; (mean age of 28.2 ±19.6 years). The most affected (23.5%) was the 20-29-year old age group. On the 30th July 2014, a 26-year old male (recorded as the index case of the cholera outbreak in the Brong Ahafo region) with a travel history to Accra reported to the Nkoranza district hospital and presented with signs and symptoms suggestive of cholera. The outbreak lasted 25 weeks; peaking in the week 15 with 179 (17.3%) cases. An overall attack rate of 71/100,000 population and a case fatality rate of 2.4% were recorded in the region. Asutifi South district, however, recorded a case fatality of 9.1%, the highest amongst all affected districts. The majority of the cases reported in the region were from Atebubu-Amanten (31.1%), Sene West (26.2%), Pru (18.2), and Asunafo North (9.9%) districts. Vibrio cholerae serotype O1 was isolated from rectal swabs/stool samples. Conclusion : vibrio cholerae serotype O1 caused the cholera-outbreak in the Brong Ahafo Region and mainly affected young adult-males. The most affected districts were Atebubu-Amanten, Sene west, Pru (located in the eastern part of the region), and Asunafo North districts (located in the south west of the region). Case Fatality Rate was higher (2.4%) than the WHO recommended rate (< 1%). Active district level public health education is recommended for prevention and effective response to future outbreaks of cholera.

What’s the Difference? 2D DIGE Image Analysis by DeCyderTM versus SameSpotsTM

The efficiency and reproducibility of two-dimensional difference gel electrophoresis (2D DIGE) depends on several crucial steps: (i) adequate number of replicate gels, (ii) accurate image acquisition, and (iii) statistically confident protein abundance analysis. The latter is inherently determined by the image analysis system. Available software solutions apply different strategies for consecutive image alignment and protein spot detection. While DeCyder^TM performs spot detection on single gels prior to the alignment of spot maps, SameSpots^TM completes image alignment in advance of spot detection. In this study, the performances of DeCyder^TM and SameSpots^TM were compared considering all protein spots detected in 2D DIGE resolved proteomes of three different environmental bacteria with minimal user interference. Proteome map-based analysis by SameSpots^TM allows for fast and reproducible abundance change determination, avoiding time-consuming, manual spot matching. The different raw spot volumes, determined by the two software solutions, did not affect calculated abundance changes. Due to a slight factorial difference, minor abundance changes were very similar, while larger differences in the case of major abundance changes did not impact biological interpretation in the studied cases. Overall, affordable fluorescent dyes in combination with fast CCD camera-based image acquisition and user-friendly image analysis still qualify 2D DIGE as a valuable tool for quantitative proteomics.

An outbreak of rubella at rural area of West Bengal, India in 2016

Rubella is a contagious, generally mild viral infection that occurs most often in children and young adults. The infection in pregnant women may cause foetal death or congenital defects known as congenital rubella syndrome (CRS). Globally, the incidence of CRS was from 0.4-4.3/1000 live birth. The aims of the study were to (i) investigate outbreak to determine the magnitude of outbreak, and (ii) analyze outbreak in term of time, place and person distribution, and recommend preventive measures. We conducted investigation by house to house active case search at Dumra village of Purba Medinipur district, West Bengal; India between February and May’ 2016. Epidemic curve was drawn to see the dynamic of outbreak and spot map was plotted to see the distribution of cases. Blood specimen was taken for serological test of virus. A total of 54 cases were identified; among those five cases were found positive for rubella (IgM). The attack rate (AR) among the women more 7.5% (27/358) than men 7.0% (27/388), with overall AR was 7.2% (54/746). Maximum cases were found among age group of 5-9 years with AR was 8.0% (23/286), followed by age group 0-4 years with AR was 3.9% (12/309). Median age was 8 years with rage from 0.6 to 33 years. Seventeen percent (9) cases were among female reproductive age (15-33 years). None of them were pregnant. Under five children were 33% (18/54) and 72% (13/18) were vaccinated with measles but none of them were vaccinated with rubella vaccine. All the patients were treated at home and at outpatient department. Outbreak of rubella was confirmed. We recommended introduction of rubella vaccine in National Immunization Schedule of India.South East Asia Journal of Public Health Vol.7(1) 2017: 17-22

Searching possible accident black spot locations with accident analysis and GIS software based on GPS coordinates

There are several methods for the analysis of road accidents in a road network. In Hungary from 2011 GPS coordinates are used to identify the location of personal injury accidents. This method significantly improves the display of locations of accidents on the map, which can be then analyzed using GIS tools. Accident black spots are the most dangerous places in road networks identified by the density of the accidents in the network. One of the analysis methods is the accident density searching. The methods and algorithms used in some software may show differences in relation to one another. The aim of this research is comparing two applications by investigating the local road network in Győr. The analysis was made using the WEB-BAL accident analysis software using the density-based spatial clustering of applications with noise procedure and the QGIS software using the kernel density estimation method. The former is the official accident database and online software used for accident investigations and the latter is an open source geographic information system. The results are visualized in accident density plots and black spot maps.

Crowdsourcing photograph locations for debris flow hot spot mapping

This study shows the feasibility of obtaining hazardous hot spot information on landslide and debris flow from crowdsourced data. Historical hazard or disaster photographs were voluntarily uploaded by the public to a Web photograph album. A total of 2245 hazard photographs from 1973 to 2015 were crowdsourced, and each photograph was tagged with geographical coordinates. After the removal of outliers, 96% of the photograph points were found within the 4 km potential debris flow buffer of existing databases, and none was found along the steep slopes with a mean of 14°. The photograph hot spot analysis using local Moran’s I or G i * was identified statistically significant without subjective judgment. The DBSCAN model was also used to detect hot spot clusters effectively. The model parameters were nearly automatically generated on the basis of the count plot and the nearest neighbor distance graph. The results of these approaches were generally consistent with the hazardous hot spot maps and strongly related to central and southern Taiwan from the crowdsourced photograph data. Results reveal that the hot spot areas are found in areas with faults and near the potentially weak and fractured rocky regions. The majority of the landslides occur near the fault line because the strong ground motions triggered by an earthquake propagated along the fault rupture plane. Hot spot mapping using crowdsourced data can be used to estimate where debris flow will frequently occur and show how large the debris flow will be. Potentially hazardous areas can be effectively determined by the hot spot analysis of crowdsourced data.

Focus on materials, semiconductors, vacuum, and cryogenics

Focus on materials, semiconductors, vacuum, and cryogenics

The heritage and cultural values of ancient Chinese maps

Ancient Chinese cartography dates back to the Western Jin Dynasty in the third century. Cartography was initiated during this period by Pei Xiu, a minister and cartographic expert, who suggested six principles of cartography. Pei Xiu, who is known as the father of Chinese cartography, oversaw the completion of the “Yu Gong Regional Maps”, along with 18 articles and the “Terrain Fangzhang Map” (AD 224–271). This led to a number of subsequent cartographic initiatives including the “Wooden Fangzhang Map” (an administrative map) which was completed by Xie Zhuang, a minister during the Southern Dynasties (AD 502–557), “Haineihuayi Tu” (Map of China and its neighbouring countries) (AD 730–805), drawn by Jia Dan, a cartographer of the Tang Dynasty, and “Shouling Tu” (an administrative map, AD 1031–1095), drawn by Shen Kuo, a scientist during the Northern Song Dynasty. Throughout the 16th century, ancient Chinese cartography developed continuously and cumulatively, and formed the specific characteristics of China ancient cartography. Although Western latitude- based and longitude-based mapping techniques introduced to China in AD 1460, the theories and methods of Chinese and Western mapping systems co-existed for over 400 years. The cultural heritage of Chinese cartography can be seen in many famous ancient Chinese maps, including astronomical figures and atlases (world, Chinese, regional, military, water conservancy, historical, and educational maps), charts, and maps of scenic spots. These have hitherto been kept in well-known archives and institutions across the world. They form an important part of the global cultural heritage of ancient maps and cartography. Given their high cultural value, these maps remain an important point of study. This paper provides a preliminary discussion on the rarity, application, and the historical, scientific, and artistic value of ancient Chinese maps.