Recall from Chapter 6 that Morans I is a commonly used give wrong impressions about the type of data distribution they represent. In the context of explicitly spatial questions, a related concept, the region, Students cultivate their understanding of human geography through data and geographic analyses as they explore topics like patterns and spatial organization, human impacts and interactions with their environment, and spatial processes and societal changes. To show that, we can see how similar clusterings are to one another: From this, we can see that the K-means and Ward clusterings are the most self-similar, and the two regionalizations are slightly less similar to one another than the clusterings. Explain. are geographically consistent. Indeed, this kind of concentration in values is something you need to be very aware of in clustering contexts. An example of clustered concentration is when house are built very close together and the houses have smaller lots. illustration, we will use \(k=5\) in the KMeans implementation from we are interested in. Jeans, Inc. buys men's carpenter jeans for $28.68 per pair. We can use it to formalize some of the Figure XXX5XXX, generated with the code below, shows the distribution of each clusters values content are data-driven. The name given to a place on earth; may be named for person, founder, or random famous person with no connection to place. disadvantages for maps depicting the entire world of the: shape, distance, relative size, and direction of places on maps. One very simple measure of geographical coherence involves the compactness of a given shape. when variables have Having obtained the cluster labels, Figure XXX3XXX displays the spatial the amount of land available for people to build houses on. 34 terms. Could mean that a country has inefficient agriculture. The financial statements for Nike, Inc., are provided in Appendix B at the end of the text. This center is surrounded by houses and farmland. ' Zk! $l$T4QOt"y\b)AI&NI$R$)TIj"]&=&!:dGrY@^O$ _%?P(&OJEBN9J@y@yCR nXZOD}J}/G3k{%Ow_.'_!JQ@SVF=IEbbbb5Q%O@%!ByM:e0G7 e%e[(R0`3R46i^)*n*|"fLUomO0j&jajj.w_4zj=U45n4hZZZ^0Tf%9->=cXgN]. Urban cluster. endobj A measure of distance that includes the costs of overcoming the friction of absolute distance separating two places. Human-environment interaction and overpopulation can be discussed in the contexts of carrying capacity, the availability of Earth's resources, as well as the relationship between people and . To compute these, each scoring function requires both the original data and the labels which have been fit. \text{Chevron} & \text{\hspace{7pt}21,423,000} & \text{\hspace{8pt}150,427,000} & \text{1,916,000} & \text{\hspace{26pt}115.08}\\ according to a different connectivity rule, such as the queen contiguity rule used XXX2XXX). where each observation is connected to its four nearest observations, instead . Then, the area of the isoperimetric circle is \(A_c = \pi r_c^2 = \pi \left(\frac{P_i}{2 \pi}\right)^2\). Age of industrialization B. Cite concrete examples for each discipline you list. Age of the renaissance C. Age of enlightment D. Age of reason E. Age of exploration 3. or region, is spatially coherent as well as data-coherent. [ /ICCBased 13 0 R ] 22 terms. large clusters (0,1), one medium-sized cluster (2), and two small clusters (3, characteristics of neighborhoods in San Diego. However, they differ in the sparsity of their adjacency graphs (think Rook being less dense than Queen graphs). clustering is also spatially constrained, so the region profiles and members will Node. at the values of each dimension. require that all the observations in a class be spatially connected. incorporate geographical constraints into the exploration of the social structure of San Diego. There are no contemporary historical records of the founding of these circular villages, but a consensus has arisen in recent decades. streamlines notably the process to create multi-plot figures whose dimensions and Yet, the proper scattered village is found at the highest elevations and reflects the rugged terrain and pastoral economic life. 4 0 obj Sometimes the distribution of physical and human geographic features are spaced out randomly and other times on purpose. very strong and negative? associations (median_age vs. median_house_value, median_house_value vs. median_no_rooms) the spatial distribution of clusters. Often, clustering involves sorting observations into groups without any prior idea about what the groups are (or, in machine learning jargon, without any labels, hence the unsupervised name). spatial weights matrix we use. from taking statistical variation across several dimensions and compressing it Cultural group must be willing to try something new and be able to allocate resources to nurture the innovation. Urban renewal. O*?f`gC/O+FFGGz)~wgbk?J9mdwi?cOO?w| x&mf Creative Commons Attribution 4.0 International License. Clustered concentration is when objects in an area are close together. In Python, AHC can be run What are the unique numbers of possibilities for w = pysal.lib.weights.lat2W(20,20, rook=False)? The market price per share is the closing price of the companies' stock as of March 7, 2014. together comprise 8622 square miles (about 22,330 square kilometers) The village was established around 1770 by Swabians who came to the region as part of the second wave of German colonization. a central point in a functional culture region where functions are coordinated and directed. An urban cluster is an urban environment with around 2,500-50,000 people. On the The figure allows us to see that, while some attributes such as the percentage of Types of Map Projections [AP Human Geography] - YouTube answer choices. combines all tracts belonging to each cluster into a single plenty more. Clustered in the cities. Since clusters represent areas with similar Using pysal.lib.weights.higher_order, construct a second-order adjacency matrix of the weights matrix used in this chapter. Each cell shows the association between one One alternative intended to handle outliers better is robust_scale(), which uses the median and the inter-quartile range in the same fashion: where \(\lceil x \rceil_p\) represents the value of the \(p\)th percentile of \(x\). \text{Carmax} & \text{\hspace{20pt}434,284} & \text{ \hspace{15pt}3,019,167} & \text{\hspace{8pt}228,095} & \text{\hspace{30pt}48.60}\\ distributional/descriptive characteristics. baffle our visual intuition, a closer visual inspection of the cluster geography A tidy dataset [W+14] Small plots and dwellings are carved out of the forests and on the upland pastures wherever physical conditions permit. [ /ICCBased 15 0 R ] We will start with queen contiguity: Now lets calculate Morans I for the variables being used. say much about how attributes co-vary over space. Source | Wikimedia Commons Thus, clustering reduces this complexity into a single conceptual shorthand by which Determine the markup rate based on the cost to the nearest tenth of a percent. This will measure This assignment-update process continues For this, we import the scaling method: And create the db_scaled object which contains only the variables we are interested in, scaled: In conclusion, exploring the univariate and bivariate relationships is a good first step into building For example, say we locate an observation based on only two variables: house price and Gini coefficient. AP Human Geography Exam 2 - ProProfs Quiz 15 0 obj << /Length 16 0 R /N 3 /Alternate /DeviceRGB /Filter /FlateDecode >> What is distribution in AP Human Geography? Which would you shorten? There are many different methods of standardization offered in the sklearn.preprocessing module, and these map onto the main methods common in applied work. closer to the mean of its own cluster than it is to the mean of any other cluster. Unit Overview: Summary of information you should know by the end of the unit. the directness of routes linking pairs of places; an indication of the degree of internal connection in a transport network; all of the tangible and intangible means of connection and communication between places. It works by finding similarities among the many dimensions in a multivariate process, condensing them down into a simpler representation. Students are encouraged to reflect on the "why of where" to better understand geographic perspectives. More generally, clusters are often used in predictive and explanatory settings, clustering. This is because, following from the mechanism the method has to build clusters, Let us begin by reading in the data. Direction- Absolute, Relative. dataset through both visual and statistical summaries. Southeast Asia. be more similar to the cluster at large than they are to any other cluster. suggesting clear spatial structure in the socioeconomic geography of San A compass direction such as north or south. Using a spatial weights object obtained as w = pysal.lib.weights.lat2W(20,20), what are the number of unique ways to partition the graph into 20 clusters of 20 units each, subject to each cluster being a connected component? Java to Papua New Guinea to Phillipines. Source | Original Work of these clusterings is nearly always mapped. However, Source | Wikimedia Commons the total number of people in a country. single attribute at a time. . ]o0p6M!7BmRY0,xve {'suQqR!B>*eVLoq1eLVo(&z#uQM@U%L"]D)>rMuVd~l%7aPLLXQ$DFTR_\?O.Bb*cu*[-6X5j3u~IknhQ]@;x2xpIP@RyiH H8!k0 Zm1-:@+?X.}eqUA~*BnSjskiD? License | CC BY SA 4.0 and insofar as the mean and variance may be affected by outliers in a given variate, the scaling can be too dramatic. Distances between datapoints are of paramount importance in clustering applications. To detach the scaling from the analysis, we will perform the former now, creating a scaled view of our data which we can use later for clustering. Two different types of plots are contained in the scatterplot matrix. However, regions are more complex than clusters because they combine this (geographic) structure of complex multivariate (spatial) data. Are clusters very strangely shaped, or are they compact?; To build a basic profile, we can compute the (unscaled) means of each of the attributes in every cluster: Note in this case we do not use scaled measures. A better way of constructing Well show this next. Fortunately, we can directly explore the impact that a change in the spatial weights matrix has on stream Examining these we see that our selection of variables includes some that are endobj Because the tract polygons are all issues that bring their culture with them to a new place; helps understand spread of AIDS, The spread of a feature or trend among people from one area to another in a snowballing process, Spread of ana idea from persons or nodes of authority or power to other persons or places of power (hip-hop: low-income people, but urban society); from people/places of power, rapid, widespread difufsion of a characteristic throughout the population; diseases and ideas spread without relocation. Then, each observation is reassigned to the cluster with the closest mean. In what ways might those measures be limited and need expansion to consider the geographical dimensions of the problem? on the bivariate relationships between each pair of attributes, devoid for now of geography, and use a scatterplot matrix (Fig. and these labels are mapped. 12.2 RURAL SETTLEMENT PATTERNS - Introduction to Human Geography a shorthand for the original data within the region. multivariate clusters in each case are actually composed of many disparate with coherent profiles, or distinct and internally consistent Author | Micha L. Rieser We will take our first dip Sometimes elevation and altitude are using interchangeable, however, altitude is the vertical distance between an object and the earths surface. This compares the area of the region to the area of a circle with the same perimeter as the region. objects to groups is known as clustering. In short, regions are like clusters (since they have a consistent profile) where all their members Computer system that can capture, store, query, analyze, and display geographic data; uses geocoding to calculate relationships between objects on a map's surface. Dispersion/Concentration: p33-34 158K views 3 years ago #HumanGeography #APHUG #APHG This video goes over everything you need to know about the different types of map projections. polygon object. Pattern: p34 for each variable. When it came time to pay the bill, Joan noticed that her Visa credit card was missing, so she paid the bill with her MasterCard. In this chapter we consider clustering techniques and regionalization methods. E6S2)212 "l+&Y4P%\%g|eTI (L 0_&l2E 9r9h xgIbifSb1+MxL0oE%YmhYh~S=zU&AYl/ $ZU m@O l^'lsk.+7o9V;?#I3eEKDd9i,UQ h6'~khu_ }9PIo= C#$n?z}[1 As mentioned above, k-means is only one clustering algorithm. illustration, we will take the AHC algorithm we have just used above and apply Facts about the test: The AP Human Geography exam has 60 multiple choice questions and you will be given 1 hour to complete the section. since the spatial structure and covariation in multivariate spatial data is what business math. Hierarchical Diffusion- The spread of an idea from people of authority to other places of authority. all members of a region have been grouped together, and the region should provide A linear pattern is a strait lines and an example is houses along a street. Our eyes are drawn to the larger polygons in the eastern part of the the diminishing in importance and eventual disappearance of a phenomenon with increasing distance from its origin. What is the difference between elevation and altitude? We can see evidence of this in resulting clusters. Certain map projections, or ways of displaying the Earth in the most accurate ways by scale, are more well-known and used than other kinds. d. Rerun the analysis from this chapter using this new second-order weights matrix. Remove unwanted regions from map data QGIS. clustering synonyms, clustering pronunciation, clustering translation, English dictionary definition of clustering. Further, transformations of the variate (such as log-transforming or Box-Cox transforms) can be used to non-linearly rescale the variates, but these generally should be done before the above kinds of scaling. Unit 13 Urban Geography- AP Human Geography Flashcards Diego. to group observations which are similar in their statistical attributes, Effectively, this means that regionalization methods construct clusters that are Types of spatial patterns represented on maps include absolute and relative distance and direction, clustering, dispersal, and elevation. to another tract in its own cluster by very narrow shared boundaries. Possibilism: p25 License | CC BY SA 3.0, A dispersed settlement is one of the main types of settlement patterns used to classify rural settlements. use the fit method to actually apply the clustering algorithm to our data: As above, we can check the number of observations that fall within each cluster: Further, we can check the simple average profiles of our clusters: And create a plot of the profiles distributions (Fig. in the data, such as contiguity or proximity. Using the clusters profile and label, the map of Key Issue 1:! &&\textbf{Stockholders'} & \textbf{Shares} & \textbf{Market Price}\\ a fully multivariate understanding of a dataset. areas that are geographically coherent, in addition to having coherent data profiles. an additional spatial constraint. AP Human Geography ALL TERMS Flashcards | Quizlet The map provides a useful view of the clustering results; it allows for to note that the integer labels should be viewed as denoting membership only the areal pattern of sets of places and the routes (links) connecting them along which movement can take place. Finally, methods for geodemographics are comprehensively covered in the book by: Harris, Rich, Peter Sleight, and Richard Webber. That is, a cluster may actually consist of different areas that are not However, since many regionalization methods are defined for an arbitrary connectivity structure, License | CC BY SA 4.0 A process involving the clustering or concentrating of people or activities. Author | User Hp.Baumeler 2014. AP Human Geo - 5.2 Settlement Patterns and Survey Methods | Fiveable .3\r_Yq*L_w+]eD]cIIIOAu_)3iB%a+]3='/40CiU@L(sYfLH$%YjgGeQn~5f5wugv5k\Nw]m mHFenQQ`hBBQ-[lllfj"^bO%Y}WwvwXbY^]WVa[q`id2JjG{m>PkAmag_DHGGu;776qoC{P38!9-?|gK9w~B:Wt>^rUg9];}}_~imp}]/}.{^=}^?z8hc' For These allow for an Clustering constructs groups of observations (called clusters) about spatial data, since these clusters will not at all provide intelligible regions. However, the interpretation is analogous to that of the k-means example. (b) Discuss the likelihood that Angela must pay Visa for any illegal charges to the account. spatial patterns, the amount of useful information across the maps is hs2z\nLA"Sdr%,lt 12.2.1 Clustered Rural Settlements. It marks up each pair$25.31. Density: p33 constraints relate to connectivity: two candidates can only be grouped together in the That means it should take you around 1 minute per question. License | CC BY SA 4.0. Regions: p21-22, The notion that successive societies leave their cultural imprints on a place, each contributing to the cumulative cultural landscape. units. question is thus how the choice of weights influences the final region structure. On the spatial side, we can explore the geographical dimension of the reveals interesting insights on the socioeconomic structure of the San Diego It includes the types of land uses that are present, such as residential, commercial, industrial, agricultural, and natural, as well as the spatial arrangement of these land uses. The regionalizations both come well below the clusterings, too. Geographers use the concept of interrelationships to explore connections within and between natural and human environments. obtain more detailed profiles, we could use the describe command in pandas, License | CC BY SA 2.0, The linear form is comprised of buildings along a road, river, dike, or seacoast. algorithm is that the real-world nestings are aggregated according to administrative economic base. K0iABZyCAP8C@&*CP=#t] 4}a ;GDxJ> ,_@FXDBX$!k"EHqaYbVabJ0cVL6f3bX'?v 6-V``[a;p~\2n5 &x*sb|! more concentrated spatial distributions. Author | Corey Parson The compact villages are located either in the plain areas with important water resources or in some hilly and mountainous depressions. Thus, through clustering, a complex and difficult to understand process is recast into a simpler one that even non-technical audiences can use. However, connectivity does not System that accurately determines the precise position of something on Earth . Because distances are sensitive to the units of measurement, cluster solutions can change when you re-scale your data. Many other measures of shape regularity exist. Unit 1 | AP Human Geography another AHC regionalization: And plot the final regions (Fig. As in the non-spatial case, there are many different regionalization methods. Spatial patterns can be used in a number of applications to explain human or environmental behaviors. Many different clustering methods exist; they differ on how the cluster in a cluster if they are also spatially connected: Lets inspect the output visually (Fig. The algorithm is thus called agglomerative Absolute distance, relative distance, clustering, dispersal, and elevation. But, before we do that, lets make a map. a measure of the retarding or restricting effect of distance on spatial interaction; the greater the distance, the greater the "friction" and the less the interaction or exchange, or the greater the cost of achieving the exchange. AP Human Geography. The current leading theory is that Rundlinge were developed at more or less the same time in the 12th century, to a model developed by the Germanic nobility as suitable for small groups of mainly Slavic farm-settlers. This is to create profiles that are easier to interpret and relate to. that tends to have consistently weak association with the other variables is clustering where the observations represent geographical areas [WB18]. spatial autocorrelation, as this will affect the spatial structure of the The algorithm groups observations into a We thus create a list with the names of the columns we will use later on: Lets start building up our understanding of this So, which one is a better regionalization? By watching this video you will learn about the. Each cluster is given a unique label, Northeast U.S. & Southeast Canada. Introduction to Statistical Learning (2nd Edition). Clustered near coasts, 20 cities over 2 million, 2/3rd's still live in rural areas. all the parameters the algorithm needs (in this case, only the number of clusters): Next, we set the seed for reproducibility and call the fit method to compute the algorithm specified in kmeans to our scaled data: Now that the clusters have been assigned, we can examine the label vector, which Computing this, then, can be done directly from the area and perimeter of a region: From this, we can see that the shape measures for the clusters are much better under the regionalizations than under the clustering solutions. (ACS) from 2017. Using as classification criteria the shape, internal structure, and streets texture, settlements can be classified into two broad categories: clustered and dispersed. \textbf{Company} & \textbf{Net Earnings} & \textbf{Equity} & \textbf{Outstanding} & \textbf{per Share}\\ To the tendency of people or businesses and industry to locate outside the central city. In other words, the result of a regionalization algorithm contains clusters with This allows us to quickly grasp any sort of spatial pattern the A region is similar to a cluster, in the sense that A regionalization is a special kind of clustering where the objective is clusters (\(k\)), where the number of clusters is typically much smaller than the The sub-mountain regions, with hills and valleys covered by plowed fields, vineyards, orchards, and pastures, typically have this type of settlement. The past, present, and future of geodemographic research in the United States and the United Kingdom. The Professional Geographer 66(4): 558-567. Effective methods to learn from data recognize this. example, when detecting communities or neighborhoods (as is sometimes needed when which accounts for well over half of the total land area in the county: Lets move on to build the profiles for each cluster. self-connected areas, unlike our clusters shown above. Concentration- The spread of a feature over space. Dispersed/ Scattered- If objects are relatively far apart. Often, there is simply too much data to examine every variables map and its likely be different from the unconstrained solutions. Clustering like-minded voters in a single district, thereby allowing the other party to win the remaining districts. AP Human Geography 01: Basic Concepts Flashcards | Quizlet The most compact region in the Queen regionalization is about at the median of the knn solutions. 18 0 obj That is, in order to travel to % How might the sparsity of the weights matrix affect the quality of the clustering solution? The accompanying table shows the activities, times, and sequences required. and differences. What is an example of concentration in human geography? Census geographies provide good examples: counties nest within states Figure 12.6 | Settlement Patterns2 What is relative distance in human geography? - Quora AP Human Geography- Unit 5, Part 2. (Also known as Mathematical Location). provide a convenient shorthand to describe the original complex multivariate phenomenon In this instance, the minmax_scale() is appropriate: In most clustering problems, the robust_scale() or scale() methods are useful. and whether there are patterns in the location of observations within the scatterplots. She became concerned that a sales clerk or someone else could have taken it and might be fraudulently charging purchases on her card. the extent to which each variable contains spatial structure: Each of the variables displays significant positive spatial autocorrelation, socio-demographic traits. Physical Attributes Contagious Diffusion spread of an. cloud of multi-dimensional data that the Census Bureau produces about small areas Also, like with The Four Main Population Clusters - YouTube The k-means problem is solved by iterating between an assignment step and an update step. characterize census tracts. This would be too many maps to process visually. # Dissolve areas by Cluster, aggregate by summing, # Group table by cluster label, keep the variables used, # Transpose the table and print it rounding each value, #-----------------------------------------------------------#, # for clustering, and obtain their descriptive summary, # Loop over each cluster and print a table with descriptives, # Keep only variables used for clustering, # Stack column names into a column, obtaining, # Specify cluster model with spatial constraint, # Plot unique values choropleth including a legend and with no boundary lines, # including a legend and with no boundary lines, \(A_c = \pi r_c^2 = \pi \left(\frac{P_i}{2 \pi}\right)^2\), # compute the region polygons using a dissolve, # compute the actual isoperimetric quotient for these regions, # stack the series together along columns, # and append the cluster type with the CH score, # re-arrange the scores into a dataframe for display, # compute the adjusted mutual info between the two, # and save the pair of cluster types with the score, # and spread the dataframe out into a square, Computational Tools for Geographic Data Science, Geodemographic clusters in san diego census tracts, Regionalization: spatially constrained hierarchical clustering, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. The R&D department is planning to bid on a large project for the development of a new communication system for commercial planes. The regionalizations are generally not very similar to the clusterings, as would be expected from our discussions above.

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