Generating and mapping population density surfaces within a geographical information system

Abstract

The spatially discontinuous choropleth map is a poor representation of the underlying continuous distribution of population density. A possible alternative is to derive dasymetric maps at a fine spatial resolution by making use of satellite imagery in a geographical information system. However, there are cartographic problems when these maps are displayed and further processing is needed in order to obtain approximations to a continuous density surface. Isarithmic maps of these density surfaces retain a high degree of spatial accuracy while providing pleasing and highly adaptable presentations.

The methods used to generate dasymetric and isarithmic maps are readily implemented in most raster based geographical information systems. For example, the classification of remotely sensed imagery, the subsequent processing and integration of data, and most of the cartographic display, were all undertaken in this work using the low cost IDRISI GIS that operates on standard IBM PC compatible hardware.     

Extracting raster GIS data from scanned thematic maps

One of the problems which continues to trouble practical applications of GIS is the time and cost of capturing data from paper maps. Even where the data are required in raster form the complexity of most maps means that the data are first digitised in vector form, and then rasterised, a slow and error-prone process. This paper describes an alternative approach based upon readily available hardware and software in which the map is scanned using Spot Colour Scanning, and then processed using a specially designed majority filter and editing with a painting package. Tests on a small scale soil map produce results which are about 80% as accurate as digitising, but take far less time to produce. An analysis of the sources of error indicates that many of these are due to the difficulty of scanning such a complex document as a map, and suggests that more sophisticated image processing algorithms may be needed to classify the original scanned image before the application of the filter algorithm.     

Deriving Bathymetric Maps of the Shallow Coastal Water of the Gaza Strip Coastal Zone Using Passive Remotely Sensed Imagery

Bathymetry, the mapping of the shape of the surface of the seafloor, is a necessary science for coastal populations that rely heavily on the marine ecosystem for their survival and prosperity. Bathymetric maps are typically derived through a sound or light remote sensing system that is mounted on a boat or airplane that sweeps the designated study area. However, in regions with political constraints or logistical difficulties, reaching the study area and conducting in situ measurements can be difficult or impossible. Thus, using passive satellite imaging can be an alternative approach to traditional hydrographic surveying methods. As bathymetric maps are influenced by the local sea water conditions, the bottom type, and the water constituents, choosing a derivation algorithm for the satellite images which results in a robust and accurate map is very important. In this study, QuickBird imagery was selected to investigate the functionality and the accuracy of two different bathymetric derivation algorithms to obtain robust and reliable maps of the Gaza Strip coastal zone. Despite the high pollution level in the seawater of the investigated area, the findings confirmed the feasibility of obtaining reasonable accurate bathymetric maps.     

Analysing Product-Specific Behaviour to Support Process Dependent Updates in a Dynamic Spatial Updating Model

A scale‐independent database that allows derived maps to be dynamically updated from a centrally maintained data source is an appealing alternative to traditional map revision techniques, which by today's standards are costly and inefficient. This paper presents a dynamic spatial updating model that supports automated updating of non‐standard maps in a scale‐independent database‐centric map production environment. Maps derived from the database are not separate data sets, but rather active views of the database. Each derived map is displayed in a unique way by implementing cartographic operations at the map level. While the operations applied require user involvement for strategic cartographic decisions, and algorithmic initiation and control, the technique allows geographic data to be processed cartographically without affecting the geometric integrity of the database. Each time a derived map is opened it retrieves the spatial data (and updates) from the database and applies the unique cartographic representation methods that persist on the individual derived maps. Database updates are automatically triggered to cartographic products, as process dependent updates, according to their individual product‐specific behaviour. This paper investigates product‐specific behaviour (product multiplicities) and the cartographic processing requirements to support dynamic spatial updating techniques in an object‐oriented map publishing environment. These techniques are implemented in an off‐the‐shelf software environment using ArcGIS.     

Crop type mapping using LiDAR,Sentinel-2 and aerial imagery with machine learning algorithms

LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field - often informed by data collected during ground and aerial surveys. However, manual digitizing and labeling is time-consuming, expensive and subject to human error. Automated remote sensing methods is a cost-effective alternative, with machine learning gaining popularity for classifying crop types. This study evaluated the use of LiDAR data, Sentinel-2 imagery, aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area. Different combinations of the three datasets were evaluated along with ten machine learning. The classification results were interpreted by comparing overall accuracies, kappa, standard deviation and f-score. It was found that LiDAR data successfully differentiated between different crop types, with XGBoost providing the highest overall accuracy of 87.8％. Furthermore, the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data, with LiDAR obtaining a mean overall accuracy of 84.3％ and Sentinel-2 a mean overall accuracy of 83.6％. However, the combination of all three datasets proved to be the most effective at differentiating between the crop types, with RF providing the highest overall accuracy of 94.4％. These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.     

Gradation as a Communication Device in Area-Class Maps

This paper proposes a methodology to assess gradation as a cartographic tool for communicating information in area-class maps. The communication model is used as a theoretical foundation, suggesting distinction between errors that occur in encoding and decoding of geographic information. The proposed methodology begins with the determination of a target level of encoding error. Map alternatives are constrained to achieve this target, with gradation considered as one variable in the map production process. The result is a series of maps of equal encoding accuracy but varying in the degree of gradation represented. The individual maps of the series can then be evaluated in terms of decoding accuracy. The methodology is demonstrated by producing a series of alternative forest region maps of New York, Pennsylvania, and New Jersey based on U.S. Forest Service data on tree genus distributions. The series ranges from a 4-class graded area-class map to a 13-class crisp map. The results show gradation to be a viable alternative to the proliferation of map classes as a means of cartographic communication.     

Vineyard site suitability analysis by use of multicriteria approach applied on geo-spatial data

Abstract

In this research, multicriteria decision analysis with pairwise comparison weighting method was utilized to determine the suitable locations for vineyard plantation in Sarkoy region of Turkey. Soil maps, meteorological measurements, slope, aspect and elevation maps were used as input to conduct spatial analysis. Different methods were compared and pairwise comparison method was identified as the most appropriate method of weighting for this spatial analysis. Current vineyard areas were determined using Worldview-2 imagery and their spatial distribution compared with the resulting suitability map to determine the current suitability. Comparisons showed current vineyards were mostly established in locations where suitability map expresses low capability. Further inspection unveiled that, these low capability lands are closer to the transportation networks and city/county centres that tend to be in sea level elevations as opposed to vine grapes thriving in higher altitudes. Results also enabled providing suggestions on alternative sites for new vineyard plantation.     

Establishing Classification and Hierarchy in Populated Place Labeling for Multiscale Mapping for The National Map

Current standards for federal mapping call for use of the Geographic Names Information System (GNIS) point layer for placement of United States populated place labels. However, this point layer contains limited classification information and hierarchy information, resulting in problems of map quality for database-driven, multi-scale, reference mapping, such as maps served by The National Map Viewer from USGS. Database-driven mapping often relies simply on what labels fit best in the map frame. Our research investigates alternative sources for labeling populated places, including polygons defined by the U.S. Census Bureau, such as incorporated place, census designated place (CDP), and economic place. Within each of these polygon layers we investigate relevant attributes from the decennial and economic censuses, such as population for incorporated places and CDPs, and the number of employees for economic places. The data selected are available for the entire country to serve national mapping requirements. This combination of data allows a more refined classification of populated places on maps that better represents relative importance. Visual importance on maps through scale should derive from more than simply residential population, but also economic importance, though comparison is made to this simpler case. We differentiate a fourth category of GNIS populated place points, essentially “neighborhoods” and related features—which are not incorporated places, CDPs, nor economic places. Populated places in this fourth class do not have federally defined boundaries, necessitating an alternative method for determining hierarchy in label presentation through scale.     

A method of downscaling temperature maps based on analytical hillshading for use in species distribution modelling

Climate maps have been widely used for the construction of species distribution models. These maps derive from interpolation of data collected by meteorological stations. The sparse distribution of stations generates maps with coarse spatial resolution that are unable to detect microclimates or areas that can serve as plant or animal refuges. This work proposes a method for downscaling temperature maps using the solar radiation falling upon hillsides as predictor for the influence of relief on local variability. Solar irradiance is estimated from a digital elevation model of the study area using a routine based on analytical hillshading. Some examples of downscaling from 1 km to 25 m spatial resolution are shown. The results are compared with the surface temperature maps from Landsat 8 satellite imagery.     

Assessment of Simulated Cognitive Maps: The Influence of Prior Knowledge from Cartographic Maps

Real cognitive maps encoded by humans are difficult to study using experimental methods because they are a product of complex processes whose content and timing cannot easily be known or controlled. This paper assesses the value of using neural network model simulations for investigating cognitive maps. The study simulated the learning of mapped city locations in South Carolina from reference sites in the three primary regions of the state using Kohonen self-organizing maps. The learning performances of models were considered based on available prior knowledge. Bi-dimensional regression analyses were used to assess the congruity of the simulated cognitive maps with a cartographic map and with sketch maps produced by human subjects. Error analyses indicated differences between central and peripheral reference sites. The cities known by subjects living at a central location were more evenly distributed in space and associated with significantly smaller errors. Models that learned combined state boundary and interstate highway information as prior knowledge or simultaneously with city locations consistently produced the best simulation results. The results indicated simulated cognitive maps could be used effectively to study the acquisition of spatial knowledge.     

Communicating climate change: spatial analog versus color-banded isoline maps with and without accompanying text

People discount projected impacts of climate change that they see as spatially or temporally remote. To overcome this, climate change outreach must communicate impacts as local, concrete, immediate, and situated in a well-understood frame of reference. Spatial-analog mapping may meet this challenge: by drawing on people’s experience of existing climates, this technique matches a locality’s projected climates with present-day climates of other localities. However, analog maps’ effect on climate impact perceptions has not been compared with the effect on climate impact perceptions elicited by standard climate change mapping techniques. Accordingly, this study considers whether residents of Centre Region, Pennsylvania, who are shown spatial-analog maps for future Centre Region temperatures, perceive impacts as more salient than do residents shown the same temperature-change information directly using color-banded isallotherm maps. It also considers how responses differ when this information is presented using only text, only maps, or both maps and text. An online survey of 3094 members of 11 Centre Region organizations presents the maps and/or text and then assesses respondents’ impact perceptions. Based on 444 valid responses, the study finds that respondents, using spatial-analog survey forms, generally expect impacts to be less severe and disruptive than respondents using temperature-change forms. It also finds that respondents using survey forms with maps generally expect impacts to be more severe and disruptive than respondents using text-only forms. Climate change communicators who wish to improve understanding and engagement should therefore strongly consider using maps where possible, but should exercise caution before using spatial-analog approaches.     

基于SuperMap的全球晕渲图设计与制作

晕渲图的设计与制作一直是地学工作者研究的热点问题,使用晕渲图展示地形直观、形象、立体感强.目前有多种思路被提出用于改善晕渲图的显示效果,但是在制作小比例尺晕渲图时,仍然存在一些问题,如地形破碎导致难以把握地形宏观骨架,缺少海洋地形或海洋地形过于具体干扰到陆地要素表达,传统的DEM与山影数据叠加的模式使得美观与地形清晰无法兼得,这就需要重新对这些问题进行思考.本文以晕渲图的基本理论为出发点,采用地貌晕渲与分层设色相结合的方法,讨论全球范围的晕渲图制作过程中,不同显示比例尺下DEM分辨率配置技巧、海洋地形展示方法与地形可视化新思路.实验证明,该方法可以保证晕渲图在每级显示比例尺下都能展示出良好的地形效果,为中小比例尺晕渲图增强表现力提供了参考.     

Land-use classification using ASTER data and self-organized neutral networks

Operationally AVHRR and TM/TM+ data were used and a supervised maximum likelihood classification (MLH) was applied to depict land use changes in Beijing, providing basic maps for planning and development. With rapid growth of the city these are helpful to deal with higher resolution data, whereas new classification algorithms produce land use maps more accurate. In the paper, new sensor ASTER data and the Kohonen self-organized neural network feature map (KSOM) were tested.The TSOM classified 7% more accurately than the maximum likelihood algorithm in general, and 50% more accurately for the classes ‘residential area’ and ‘roads’. The results suggest that ASTER data and the Kohonen self-organized neural network classification can be used as an alternative data and method in a land use update operational system.     

Practical Application of 3D Visualization Using Geotechnical Database: A Case Study Karsiyaka (Izmir) Settlement Area (Turkey)

3D geographic information system software’s (GIS) are widely used in engineering geology applications. This study was performed in the Karsiyaka settlement area for the preparation of engineering geological maps and evaluation of geological structures. Firstly, topographic maps digitized with Arcview GIS 3.2. Engineering geological maps were prepared using site works and digitized with the Rockworks 2006 programme and later stored in GIS-based computer systems. 3D modelling analysis and assessment using a geotechnical database is important to assist decision-making for land use and metro subway line planning, construction site selection, selection of water sources, etc. In this respect, the sub-surface of the study area is fully 3D visualized and useful soil class zonation maps for different depths maps are performed to be used in further studies. At last, after research at this site, the construction applications of Karsiyaka have multiplied.     

地理信息系统在江苏省公益林分布图制作中的应用

运用地理信息系统(GIS)研制了江苏省公益林分布图,总结了公益林分布图制作的原理、方法、技术路线和特点。为江苏省公益林进行信息化管理奠定了基础,为发展高效林业提供了科学的决策和管理平台。     

Integration of the MODIS Snow Cover Produced Into Snowmelt Runoff Modeling

Because of the difficulty of monitoring and measuring snow cover in mountainous watersheds, satellite images are used as an alternative to mapping snow cover to replace the ground operations in the watershed. Snow cover is one of the most important data in simulation snowmelt runoff. The daily snow cover maps are received from Moderate Resolution Imaging Spectroradiometer (MODIS), and are used in deriving the snow depletion curve, which is one of the input parameters of the snowmelt runoff model (SRM). Simulating Snowmelt runoff is presented using SRM model as one of the major applications of satellite images processing and extracting snow cover in the Ghara - Chay watershed. The first results of modeling process show that MODIS snow covered area product can be used for simulation and forecast of snowmelt runoff in Ghara - Chay watershed. The studies found that the SCA results were more reliable in the study area.     

A SEMI-GRAPHIC CONSTRUCTION FOR THE CONICAL ORTHOMORPHIC PROJECTION WITH ONE STANDARD PARALLELt

Abstract

The advantages of orthomorphic projections for topographical maps, whether for civil or military uses, have been increasingly realised in recent years. This has led to their gradual adoption as bases for map series by the leading countries, the conical type having been adopted in France for the new 1 : 50,000 maps, in Canada for the National Defence maps and in Britain by the Directorate of Military Survey for some useful European series. Appreciation of the nature and properties of graticules can best be achieved through an understanding of the theory of the projection system, whether the exposition be mathematically or partly graphically developed. Whereas the cylindrical and zenithal types of the orthomorphic projections have some explanation in graphical form there has been no comparable alternative to the difficult mathematical treatment of the conical type. The semi-graphic method presented here has been developed to contribute towards such a graphical exposition and in its method demands only a modest mathematical ability.     

地图学的恒常性和易变性

在历次科技革命的推动下,地图由曾经的无所不能的珍稀品逐渐演变成无所不在的生活必需品,服务于不断增加的移动人口,而地图学依然是制作和使用地图的科学、艺术和技术。本文重访地图学4个恒常的核心议题:何为地图,地图何用,如何制图,谁在制图,笔者回顾了自20世纪70年代计算机引入地图学以来地图品种、地图功能价值、制图流程以及制图者角色的发展过程,举例说明贯穿其中的恒常性和易变性,并指出在云计算平台支持下,一个可持续的制图生态环境正在形成。     

SAR立体影像匹配的视差图融合方法

提出了一种基于视差图融合的匹配方法。首先,基于归一化互相关系数(normalized cross correlation,NCC),利用多个不同尺寸的匹配窗口分别进行匹配,获取相应的视差图;然后,提出了一种左右一致性(left right consistency,LRC)和信噪比(signal to noise ratio,SNR)相结合的置信测度,用来评价视差图中每个视差的置信水平;在此基础上,提出了一种视差图融合策略,该策略对上述多个匹配窗口获取的视差图进行加权融合,融合时既考虑了视差本身的置信水平,也兼顾了其邻域视差的影响。采用TanDEM-X的聚束立体影像进行试验,结果表明,本文方法能有效减少DEM粗差点,DEM高程精度由11.28 m提高到8.41 m。