Historical Features Of Spatial Data Essay

Place is a general area. A place can be described by the geographical markers around it and the “things” that make it unique. (A city could be built over the same area that a previous city was built on)

Some of the major technologies used in geography, have made both sorts of information far more readily available and far easier to use. Statistical analysis and modeling of spatial patterns have relied on computer technology.

Physical geography is the various physical mechanisms and natural developments that create the structures of Earth. The physical geography of places vary because of the natural components they consist of such as their latitude, soil, water, earth structures, and much more. I will describe a general overview of Brownsville, TX’s physical geography; how the physical geography affects the weather and climate of Brownsville, TX; how the latitude affects the daylight hours, seasons, and etc.; the soils and water sources near the city; and how all this affects the lifestyle and economy of the people.

Our method not only memory and time efficient on symbolic datasets. It also effective to mine significant patterns from the datasets. Figure \ref{symbolic}(c) illustrated the number of pattern of FPPM method on Bike sharing data is 1,529 whereas our approach mine only 79 periodic patterns that is 19 times less. The others Oil price and Diabetes our method mine 9 times and 4.5 times less patterns than the existing method. In this discussion we said that our method is efficient in symbolic datasets in both noisy and noiseless data.

4. Analyzing geographic information: involves seeking patterns, relationships and connections, noting such things as similarities, trends and differences over space and time

What can maps tell us about spatial changes over time? Historical maps can help us better understand the changes we have made overtime to the landscape and possibly, how to better manage these changes sustainably. Topographical maps can help us envision the changes to the mountains and hills that we have made and the interactions we have had with our lithosphere and possibly how to better interact with it.

The k-function gives insight on the spatial dependence between the locations of fast-food restaurants and schools by comparing the calculated value to an expected value based on the premise of no spatial relationship between the two. This comparison was displayed on a graph showing the expected k-function value, the observed k-function value, and a 95% confidence interval. The observed value fell above both the expected k-function value and the upper boundary of the confidence

The authors of [1] aim to dispel some of the current hype surrounding big data, mainly the misnomer that it is all about technology and the process is automated. In fact there are three critical elements requiring human expertise 1) the data must be the right kind, of sufficient quantity, and clean 2) a specific process must be followed for success starting with the identification of the objective 3) expert humans who know how to use the technology, execute the big data process, and perform the mining tasks which require significant mathematical calculations.

Spatial data is a costly resource to generate and maintain, spatial data consumers have been unable to accurately and conveniently link with other spatial data users to share and discover useful and relevant data. There is problem of insufficient and inappropriate metadata available for the clearinghouse, metadata problems impact on effective spatial data use. The following are examples of issues associated with spatial metadata:

Geodemography refers to the data collection of small areas, usually neighborhoods, based on the assumption that people with similar wants and needs tend to live close together (Solomon, 2011). I live in a small, rural town on the Tennesse River. An analysis of my neighborhood will show that each neighbor owns at least twenty acres of land, possesses a tractor, has acquired a four wheeler or side by side, and each household owns at least one gun. Geodemographic classifications are more highly dimensional than the examples I have given and include anywhere from a dozen to several hundred empirically derived characteristics (Singleton & Spielman, 2014).

How data mining can assist bankers in enhancing their businesses is illustrated in this example. Records include information such as age, sex, marital status, occupation, number of children, and etc. of the bank?s customers over the years are used in the mining process. First, an algorithm is used to identify characteristics that distinguish customers who took out a particular kind of loan from those who did not. Eventually, it develops ?rules? by which it can identify customers who are likely to be good candidates for such a loan. These rules are then used to identify such customers on the remainder of the database. Next, another algorithm is used to sort the database into cluster or groups of people with many similar attributes, with the hope that these might reveal interesting and unusual patterns. Finally, the patterns revealed by these clusters are then interpreted by the data miners, in collaboration with bank personnel.4

Here the authors try to describe the use of GIS software for location site analysis. They are trying to tell that one can feed multiple layers of information in the GIS software laid on top of each other and then all this information can be analyzed together. Every layer can contain some data like rent, cost of labor, geographic information etc.

Keywords— Trajectory mining, Association Rule mining, Frequent Pattern mining, Location Based Services (LBS), Semantic analysis

A spatial weight matrix presents the pairwise spatial relationship between two any spatial units. It has two different types such as contiguity-based weight matrix and distance-based weight matrix. Although it can be chosen by researcher’s judgement, distance-based weight matrix is more appropriate when the size of spatial units is irregular (e.g., in the case of census tracts). Hence, the spatial weight matrix used in this analysis is expressed as the follows;

have used spatial data to characterize geographic distribution, spatial pattern of field data (Ripley, 1981; Meyers, 1988; Cressie, 1991 & 1993). More recently, spatial statistics has evolved from descriptive, to predictive and now optimization models this is critical in making better choices and accurately making decisions. For example,