Discovering Spatial Patterns in the Landscape - Essay Sample

Introduction

A spatial pattern is a placement or display of objects on Earth that include spaces in between the objects. Patterns are identified based on their arrangement in a cluster or a line. Spatial patterns are central to the work of geographers, regional scientists, and economists in allocating spaces within objects. An analysis of spatial patterns gives insights on how things may fall, how the incidents are distributed, and how the data relates to several features in the current landscape. This paper discusses ways through which a map can convey spatial patterns more conveniently than tables of data and textual descriptions.

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Spatial Data Infrastructure

Spatial data infrastructure is a combination of procedures, standards, and policies that guide an organization to enable efficient interaction and management of geographic data. The infrastructure to hold geographic information is as necessary as building roads and telecommunication networks because of the provision of other services heavily dependent on the infrastructure (Bartlett, 2013). To develop the infrastructure for spatial data requires a combination of resources from both government agencies and non-governmental organizations.

Various geographical data is produced and shared in areas of application that comprise of transportation, natural resources, and health. The data collected is archived in multiple formats that are primarily in the form of paper maps or digital formats that are analyzed in the Geographic Information System (Bartlett, 2013). The data should be managed well to achieve better standards of decision making. It can only be attained if the data is cooperatively produced, organized, stored, and distributed using the concepts of Spatial Data Infrastructure.

Spatial Data Infrastructure enhances access to data, its use, and sharing that, in return, improves the use of geographic information in decision making at all levels. It should be noted that the National Spatial Data Infrastructure is a framework within a country, while the Global Spatial Data Infrastructure is a combination of networks among nations in an effort to serve the world. All countries need information and knowledge regarding their social and physical geography that enable better governance and informed decision-making. It is achieved through the Spatial Data Infrastructure that allows the use of data in numerous ways.

Types of Spatial Patterns

There are three forms of spatial patterns that include clustered, dispersed, and random patterns. In clustered spatial patterns, objects fall close to one another, and in dispersed spatial patterns, objects are arranged in equal distances to one another while in random spatial patterns, objects do not follow any known form of distribution. Understanding the patterns of land use will give an insight into the processes that brought about spatial patterns. For example, spatial patterns of Agricultural farms in Northern China were carried out to determine the appropriate landscape diversity towards soil erosion mitigation. As a result, the vegetation land cover was added to help in the control of soil erosion (Bartlett, 2013).

In adjacent research, spatial indices of the size of the patch, abundance, shape, and spacing have been used to quantify the landscape of forest areas under management (Ruass & Gold, 2008). Based on suggestions, spatial quantification of forest lands is helpful in the evaluation and management of wildlife habitats. Information into the spatial pattern characteristics helps answer concerns about spatial scale, boundaries, and spatial heterogeneity. When the patterns are quantified, it highlights crucial information about the processes of spatial patterns and the subsequent changes that may have resulted in the current patterns.

Analysis of patterns is performed through metrics that describe the spatial distribution of nonspatial variables of interest. Several researchers combine indices like the patch fragmentation and shape when attempting to quantify landscape patterns even though the evaluation of metrics is performed separately. Human beings can understand patterns based on the existent visual structures like the arrangement of varying shapes. It is the combination of shapes that lead to visual patterns.

Geography as a Spatial Science

The techniques applied by geographers are meant to reflect on the spatial scales for information collection, compilation, analysis, and display. The spatial scales also define the strategies of data sampling, experimental designs, data representation, and analysis. Theoretical paradigms are prone to change, and so do the techniques of empirical research. Therefore, the advancement of technology has led to improved that are applied to collect, analyze, and interpret information.

Techniques of Data Collection in Geography

The skill of observing events and phenomena is important in the attempt of Geography to represent the intricacies of the real world in an accurate format. The traditional way of observation is by direct contact of the geographer through exploration. Fieldwork is helpful when making decisions at a micro-level in research, such as a single watershed or cities, as sown in the picture below. Fieldwork is intensive and requires a considerable investment in human labor and financial resources to achieve the primary goal (Ruass & Gold, 2008). The fact that fieldwork is intense has made it impossible to perform macro-scale observations regarding the surface of the Earth. These observations are achieved through the application of remote sensing techniques that make use of sensors and airborne platforms.

Fieldwork gives room for geographers to directly observe where local data is unreliable to countercheck the validity of the present secondary sources of information like the census statistics. The increased access to remote sensing imagery was believed to be a threat to fieldwork. Still, nothing has changed because the precise interpretation of the imagery entirely depends on a detailed understanding of the patterns on the ground.

A sample representation of different data subsets for analysis and interpretationFieldwork also tests the validity of interpretations made by others who depend on remote sensing imagery just as it is advisable to test the validity of all secondary data sources. The presence of digitized data all over the internet has made it easy for students to download the information and perform their analyses (Ruass & Gold, 2008). Regrettably, these datasets do not give a detailed description of the source and reliability of the data. In cases where the meta-data is missing, the digital datasets may be useful if the researcher is in a position to assess their safety through fieldwork. However, the digital age has made fieldwork less important because it is now easy to collect observations automatically from online datasets for use in spatial patterns.

Comparisons Between Maps, Textual Descriptions and Tables of Data

A map is a model of communication that is interpreted for a better understanding of the displayed spatial pattern while a textual description is an analysis, interpretation or comparison of text in a piece of writing while tables are figures and facts that are systematically displayed to convey pieces of information. The interpretation process is taken cautiously because using maps has proven to be challenging, and more often, the interpretation is confused (Monmonier, 2018). Based on past map interpretations, challenges have been experienced in situations where coincidental associations are present between variables that have no relationship. In such cases, it is difficult to distinguish between effects and causes.

Tables are useful in organizing information in rows and columns for better viewing and presentation (Scheffers et al., 2015). A table is a versatile tool of communication that is used to present data as a standalone or can accompany other means of data representation, such as graphs. They support several parameters and are used to follow up on variables, frequencies, and associations.

The cumulative approach to allow classification of sets and a precise analysis adds semantics to the acquired results. In the field of research, text units carry different levels of granularity, and they correspond to a combination of sentences and paragraphs as well as chapters. The text units are analyzed under this process to identify their existing spatial aspect. These units comprise of three patterns that include the point of view, itinerary, and the area of comparison.

Differences Between Maps, Textual Descriptions and Tables

A map represents physical features of a given area in a diagrammatic form while tables give a list of names in rows and columns mostly used to synthesize or explain literature and textual representations are results presented in visual form (Wilkins 2010). Textual presentations are in the form of charts, drawings, photos and graphs while tables enable easier readability of a document because they eliminate numeric data. The map interpretation process entails performing analysis for the spatial information that is portrayed and the way it is portrayed. There are various methods of analyzing maps that include logical, mechanical, and intuitive (Krygier & Wood, 2011). An intuitive process of analysis is where a well-known method is used to compare results with knowledge, and mechanical interpretation is an analytical process where a series of regulations pertaining to the application is applied while logical analysis is a process where the well-thought assumptions are made, and analysts think through the steps during the whole process of analysis.

In the process of table formulation, it is important to determine the number of rows and columns that are needed to show the data in a clear form (Fischer & Wang, 2011). This process is achieved by first establishing the number of variables that are present in the data set. Methods of spatial pattern analysis are classified into three that include geostatistical data, lattice data, and point data.

Additional classification is attained by putting into consideration if the data values are quantitative or qualitative and if they are classified as univariate or multivariate. Point pattern techniques were initially applied in plant ecology and economic geography as well as epidemiology. Geostatistics is used in geology and mining as well as lattice data in econometrics. As evidence, there are hundreds of techniques of spatial pattern analysis and the accompanying texts that give their full description.

The point of view is analyzed by spatial features of several scales that form a spatially relevant group of areas, and the itinerary description forms a linear geometry and falls in order under a text unit (Waller, 2004). In contrast, the area of comparison falls in between adjacent geographical areas whose definition is underlined by two spatial features far away from one another by fall under one text unit. Researchers should know that different spatial statistics can be applied in continuous data with varying object data that include points, lines, and areas (Wilkins, 2010). The type of available data defines the type of spatial statistics that are applied to achieve the best results from spatial analysis.

References

Anselin, L. (2013). Spatial econometrics: methods and models (Vol. 4). Springer Science & Business Media.

Bartlett, M. S. (2013). The statistical analysis of spatial pattern (Vol. 15). Springer Science & Business Media.

Dale, M. R. (2000). Spatial pattern analysis in plant ecology. Cambridge university press.

Fischer, M. M., & Wang, J. (2011). Spatial data analysis: models, methods and techniques. Springer S...