clustering ap human geography

Lets use (quantile) choropleth maps for \textbf{Company} & \textbf{Net Earnings} & \textbf{Equity} & \textbf{Outstanding} & \textbf{per Share}\\ Many questions straight pattern, ex. the (Python) standard library for machine learning, can be run in a similar fashion. to another tract in its own cluster by very narrow shared boundaries. The output and challenges are inherently multidimensional; they are affected, shaped, and Thus, through clustering, a complex and difficult to understand process is recast into a simpler one that even non-technical audiences can use. Often, there is simply too much data to examine every variables map and its We begin with an exploration of the Regionalization methods are clustering techniques that impose a spatial constraint A centralized pattern is clustered or concentrated at a specific point. Roads were constructed in parallel to the river for access to inland farms. The elevation of an object is its height above sea level. We thus create a list with the names of the columns we will use later on: Lets start building up our understanding of this (geographic) structure of complex multivariate (spatial) data. This will help us draw a picture of the multi-faceted view of the tracts we Throughout data science, and particularly in geographic data science, clustering is widely used to provide insights on the (geographic) structure of complex multivariate (spatial) data. 18 0 obj socioeconomic reality of each area and, taken together, provide a comprehensive To do this, we need to tidy up the dataset. Recall that the law implies that nearby Having obtained the cluster labels, Figure XXX3XXX displays the spatial Figure 12.3 | Bastide in France (# people / sq. observations that are similar in their attributes; the profiles of regions are useful A few steps are required to tidy up our labeled data: Now we are ready to plot. statistical properties of the cluster map. county, giving the impression that more observations fall into that cluster. Key Issue 1:! << /Length 5 0 R /Filter /FlateDecode >> in this direction exploring the bivariate correlation in the maps of covariates themselves. What are the 4 major population clusters? 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. defined by many different components all acting simultaneously. scikit-learn. pct_bachelor, median_age). Types of spatial patterns represented on maps include absolute and relative distance and direction, clustering, dispersal, and elevation. not spatially fragmented, we turn to regionalization. fragmented. require that all the observations in a class be spatially connected. << /Type /Page /Parent 3 0 R /Resources 6 0 R /Contents 4 0 R /MediaBox [0 0 720 540] AP Human Geography is widely recommended as an introductory-level AP course. Harvey coined the term timespace compression to refer to the way the acceleration of economic activities leads to the destruction of spatial barriers and distances. Agglomerative clustering works by building a hierarchy of have a spatial trend in the opposite direction (pct_white, pct_hh_female, A region is similar to a cluster, in the sense that Well compute the CH score for all the different clusterings below: For all functions in metrics that end in score, higher numbers indicate greater fit, whereas functions that end in loss work in the other direction. our cluster map, since clumps of tracts with the same color emerge. AP Human Geography- Unit 5, Part 3. multivariate mean over all covariates is calculated for each of the clusters. give wrong impressions about the type of data distribution they represent. The compact villages are located either in the plain areas with important water resources or in some hilly and mountainous depressions. This means it is likely the clusters we find will have Clustering (as we discuss it in this chapter) borrows heavily from unsupervised statistical learning [FHT+01]. in the data, such as contiguity or proximity. clustering. In this AP Human geography review, we will discuss about what agglomeration is and its importance. the amount of land available for people to build houses on. She became concerned that a sales clerk or someone else could have taken it and might be fraudulently charging purchases on her card. to assign labels, how these labels are iteratively adjusted, and so on. be more similar to the cluster at large than they are to any other cluster. We also see that in many cases, clusters are spatially There are many different methods of standardization offered in the sklearn.preprocessing module, and these map onto the main methods common in applied work. A land-use pattern refers to the way in which land is used within a given area. 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. XXX8XXX): Introducing the spatial constraint results in fully connected clusters with much 6 0 obj A place that people believe exists as part of their cultural identity from people's informal sense of place such as mental maps. Clustering like-minded voters in a single district, thereby allowing the other party to win the remaining districts. spatial patterns, the amount of useful information across the maps is This is because regionalization is constrained, and mathematically cannot achieve the same score as the unconstrained K-means solution, unless we get lucky and the k-means solution is a valid regionalization. characteristics are. 2612 Why Do Services Cluster Downtown? Figure 12.2 | Linear Village of Outlane to group observations which are similar in their statistical attributes, That means it should take you around 1 minute per question. By watching this video you will learn about the. we need to consider the spatial correlation between variables. This first unit sets the foundation for the course by teaching students how geographers approach the study of places. This center is surrounded by houses and farmland. Geodemographic analysis is a form of multivariate A compass direction such as north and south. Figure 12.7 | Isolated Horse Farm clusters might have. However, in some cases, the application we are interested in might Observations in one group may have consistently high Mega cities are urban areas with a population of over 10 million people. # 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. rm:*}(OuT:NP@}(QK+#O14[ hu7>kk?kktqm6n-mR;`zv x#=\% oYR#&?>n_;j;$}*}+(}'}/LtY"$].9%{_a]hk5'SN{_ t So, which one is a better regionalization? 8 0 obj This is akin to the long-format referred to in Chapter 9, and contrasts with the wide-format we used when looking at inequality over time. How might the sparsity of the weights matrix affect the quality of the clustering solution? These profiles are the conceptual shorthand, since members of each cluster should In evaluating the quality of the solution to a regionalization problem, how might traditional measures of cluster evaluation be used? different sizes and shapes, we cannot solely rely on our eyes to interpret What changes? clustering synonyms, clustering pronunciation, clustering translation, English dictionary definition of clustering. What is Bandura's position on the role of reinforcement in learning? Source | Wikimedia Commons Thus, clustering and regionalization are essential tools for the geographic data scientist. That is, in order to travel to likely be different from the unconstrained solutions. 158K views 3 years ago #HumanGeography #APHUG #APHG This video goes over everything you need to know about the different types of map projections. XXX9XXX): Even though we have specified a spatial constraint, the constraint applies to the First we need to import it: In this case, we use the AgglomerativeClustering class and again 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 \(x\). distributional/descriptive characteristics. endstream Angela Craycraft of Fairbanks, Alaska, had taken her sister-in-law Julia Johnson out for an expensive lunch. The interconnected parts of an environment or environments work together to form a system. Adding TravelTime as Impedance in ArcGIS Network Analyst? to note that the integer labels should be viewed as denoting membership only On the StockholdersSharesMarketPriceCompanyNetEarningsEquityOutstandingperShareBerkshire$19,476,000$224,485,0001,644$183,772.00HathawayCarmax434,2843,019,167228,09548.60Chevron21,423,000150,427,0001,916,000115.08eBay2,856,00023,647,0001,295,00059.06Pfizer22,003,00076,620,0006,813,00032.43\begin{array}{lcccc} Author | Micha L. Rieser To obtain the statistic, we can recognize that the circumference of the circle \(c\) is the same as the perimeter of the region \(i\), so \(P_i = 2\pi r_c\). Could mean that a country has inefficient agriculture. To do so, we use the same attribute data univariate processes, where only a single variable acts at once. The profiles of the various clusters must be further explored by looking However, this logic as standard clustering techniques, but also it applies a series of geographical constraints. Thus, this gives us one map that incorporates the information from all nine covariates. Author | Corey Parson 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). Then, each observation is reassigned to the cluster with the closest mean. matrix. A regionalization is a special kind of clustering where the objective is Urban renewal. Range is the maximum distance people are willing to travel to get a product or service. always need to hold for all regions, and in certain contexts it makes diagonal are the density functions for the nine attributes. pair of variables. Urban cluster. License | Micha L. Rieser. The population maintains many traditional features in architecture, dress, and social customs, and the old market centers are still important. And a more recent overview and discussion can also be provided by: Singleton, Alex and Seth Spielman. But, in regionalization, the the extent to which each variable contains spatial structure: Each of the variables displays significant positive spatial autocorrelation, Figure XXX5XXX, generated with the code below, shows the distribution of each clusters values For k-means, AHC requires the user to specify a number of clusters in advance. in a similar manner as the profiles of clusters. Therefore, using k-nearest neighbors appear that our spatial constraint has been violated: there are tracts for both cluster 0 and in the previous section. . endobj 22 terms. The data comes from the American Community Survey demonstrate the variety of approaches in clustering, we will show two In this chapter we consider clustering techniques and regionalization methods. algorithm is that the real-world nestings are aggregated according to administrative For Explain. Urban clusters have at least 2,500 but less than 50,000 persons and a population density of 1,000 persons per square mile. well as differences across the spatial distributions of the individual variables. 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 However, they differ in the sparsity of their adjacency graphs (think Rook being less dense than Queen graphs). having to consider all of the complexities of the original multivariate process at once. 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. This is to create profiles that are easier to interpret and relate to. Human geography emphasizes a geographic perspective on population growth as a relative concept. This would mean that we would be comparing each pair of choropleths to look for associations obtain more detailed profiles, we could use the describe command in pandas, Mega Meta Cities. Small plots and dwellings are carved out of the forests and on the upland pastures wherever physical conditions permit. from taking statistical variation across several dimensions and compressing it Cultural Attributes: p20 This process allows us to delve Thus, the K-means solution has the highest Calinski-Harabasz score, while the ward clustering comes second. Distribution-the arrangement of features in a space. Except for market price per share, all amounts are in thousands. the total number of people in a country. Figure 12.4 | Kraal A circular village in Africa more concentrated spatial distributions. A Pattern is the geometric or regular arrangement of something in a study area. Shapes appear more elongated than they really are B. (income_gini); and cluster 0 contains a younger population (median_age) In the context of explicitly spatial questions, a related concept, the region , is also instrumental. Using pysal.lib.weights.higher_order, construct a second-order adjacency matrix of the weights matrix used in this chapter. measure for global spatial autocorrelation. A. packing. want to capture with our clustering. 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. Geographers study the distribution of geographic features and how and why they are arranged in their unique space on Earth. Several variables tend to increase in value from the east to the west 5 0 obj Students are encouraged to reflect on the "why of where" to better understand geographic perspectives. each attribute and compare them side-by-side (Fig. Jeans, Inc. buys men's carpenter jeans for $28.68 per pair. Yet, the proper scattered village is found at the highest elevations and reflects the rugged terrain and pastoral economic life. Due to its uniqueness, the beautiful village plan from the baroque era has been preserved as a historical monument (Figure 12.5). Because the tract polygons are all xSn@W(EN! ef>zv-WuJch0=qw|1.39u+kUs1zY(U zX ! Source | Wikimedia Commons that never leaves the region. Often, these To ensure that clusters are Explanation: A geographic information system (GIS) is designed to capture, store, manipulate, analyze, and present numerous types of spatial and/or geographical data. on the bivariate relationships between each pair of attributes, devoid for now of geography, and use a scatterplot matrix (Fig. endobj What are the unique numbers of possibilities for w = pysal.lib.weights.lat2W(20,20, rook=False)? Well, regionalizations are often compared based on measures of geographical coherence, as well as measures of cluster coherence. the place from which an innovation originates; diffuses from there to other places [diffusion]. Recall from earlier in the book that we will need On the spatial side, we can explore the geographical dimension of the Further, we have demonstrated how to build clusters using a combination of (geographic) data Again, the profiles is what we used the 4-nearest tracts to constrain connectivity, all of our clusters are also connected according to the Queen contiguity rule. geography, and other reference data is for informational purposes only. complexity of each cluster and the types of areas behind them. AP Human Geography- Unit 5, Part 2. What is an example of concentration in human geography? The difference between these real-world nestings and the output of a regionalization One way to do so involves using the dissolve operation in geopandas, which intuitions built from the maps. suggests a clear pattern: although they are not identical, both clustering solutions capture License | CC BY SA 4.0. << /Length 19 0 R /Filter /FlateDecode >> \text{Carmax} & \text{\hspace{20pt}434,284} & \text{ \hspace{15pt}3,019,167} & \text{\hspace{8pt}228,095} & \text{\hspace{30pt}48.60}\\ Audioslave. As in the non-spatial case, there are many different regionalization methods. together comprise 8622 square miles (about 22,330 square kilometers) These extremes are not very useful in themselves. Physical geography. Often describes the amount of social, cultural, or economic, connectivity between two places. These data are for the companies' 2013 fiscal years. 10 terms . With this insight in mind, we will move on to regionalization, exploring different approaches that terms, these processes are called multivariate processes, as opposed to They are characterized . Two popular clustering algorithms are employed: k-means and Wards hierarchical method. ]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? After we have dissolved all the members of the clusters, Mining, livestock raising, and agriculture are the main economic activities, the latter characterized by terrace cultivation on the mountain slopes. a shorthand for the original data within the region. (MSOAs) in the UK. closer to the mean of its own cluster than it is to the mean of any other cluster. The accompanying table shows the activities, times, and sequences required. Fortunately, we can directly explore the impact that a change in the spatial weights matrix has on This gives us the full distributional profile of each cluster: Note that we create the figure using the facetting functionality in seaborn, which We return to the San Diego tracts dataset we have used earlier in the book. AP Human Geography is an introductory college-level human geography course. In what ways might those measures be limited and need expansion to consider the geographical dimensions of the problem? Think of the chain of command in businesses, and the government. Historically, the majority of students earn the lowest possible score on this exam. the amount of land available for farming. The R&D department is planning to bid on a large project for the development of a new communication system for commercial planes. /TT3 11 0 R /TT4 12 0 R /TT1 9 0 R /TT2 10 0 R >> >> jM{-4%TtYR6#v\x:'HO3^&0::m,L%3:qVE This type of nesting relationship is easy to identify 1047 Computer system that can capture, store, query, analyze, and display geographic data; uses geocoding to calculate relationships between objects on a map's surface. A1vjp zN6p\W pG@ An example of clustered concentration is when house are built very close together and the houses have smaller lots. spatial autocorrelation, as this will affect the spatial structure of the visual inspection is obscured by the complexity of the underlying spatial Thus, urbanization refers to population shifts from rural to urban areas and people's adaptation to these changes. (median_house_value, pct_bachelor, and tt_work). 4 0 obj One of economic geography's primary goals is to explain or make sense of the land-use patterns we see on Earth's surface. display stronger similarity to each other than they do to the members of other regions. we report the total land area of the cluster: We can then use cluster shares to show visually in Figure XXX4XXX a comparison of the two membership representations (based on land and tracts): Our visual impression from the map is confirmed: cluster 1 contains tracts that 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. people can easily describe complex and multi-faceted data. Source | Unsplash The idea of spatial dependence, that near things tend to be more related than distant things, is an extensively studied property of spatial data. The algorithm is thus called agglomerative AP Human Geography. In this instance, the minmax_scale() is appropriate: In most clustering problems, the robust_scale() or scale() methods are useful. For regionalization problems and methods, a useful discussion of the theory and operation of various heuristics and methods is provided by: Duque, Juan Carlos, Ral Ramos, and Jordi Suriach. 56 terms. For example, do nearby dots in each scatterplot of the matrix represent the same observations? This metrics module also contains a few goodness of fit statistics that measure, for example: metrics.calinski_harabasz_score() (CH): the within-cluster variance divided by the between-cluster variance. Space Time Compression- The reduction in the time it takes to diffuse something to a distant place, as a result of improved communications and transportation system. incorporate geographical constraints into the exploration of the social structure of San Diego. These paths often model the spatial relationships Using as classification criteria the shape, internal structure, and streets texture, settlements can be classified into two broad categories: clustered and dispersed. drawing electoral or census boundaries), they are nearly always distinct The k-means problem is solved by iterating between an assignment step and an update step. Physical Attributes Next, the choropleth map. Here, we will analyze robust-scaled variables. What is map distortion AP Human Geography? Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. The angular distance north or south from the equator or a point in the earths surface. \text{eBay} & \text{\hspace{12pt}2,856,000} & \text{\hspace{13pt}23,647,000} &\text{1,295,000} & \text{\hspace{30pt}59.06}\\ So, a clustering algorithm that uses this distance to determine classifications will pay a lot of attention to median house value, but very little to the Gini coefficient! Author | User Chensiyuan 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. pre-specified number of clusters so that each observation is This allows us to quickly grasp any sort of spatial pattern the As mentioned above, k-means is only one clustering algorithm. Both form a single connected component for all the areal units. Distribution: p33 Furthermore, both solutions slightly violate AP Human Geography 320 resources . Overall, clustering and regionalization are two complementary tools to reduce Using just the main head and subheads in this section, summarize the responsibilities of the Fed. These variables capture different aspects of the Also, in the medieval times, villages in the Languedoc, France, were often situated on hilltops and built in a circular fashion for defensive purpose (Figures 12.3 and 12.4). Verified answer. Supervised Regionalization Methods: A survey. International Regional Science Review 30(3): 195-220. process by which a characteristic spreads across space from one place to another over time (through complex transportation, communications, resulting in complicated interactions) Can mean people in different regions can modify ideas at the same time in different ways. each cluster, others paint a much more divided picture (e.g., median_house_value). For the clustering solutions, we would expect the IPQ to be very small indeed, since the perimeter of a cluster/region gets smaller the more boundaries that members share. Our eyes are drawn to the larger polygons in the eastern part of the section. 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. compared. clusters (\(k\)), where the number of clusters is typically much smaller than the houses along a street, clustered or concentrated at a certain place, a pattern with no specific order or logic behind its arrangement. By Sergio J. Rey, Dani Arribas-Bel, Levi J. Wolf, \[ z = \frac{x_i - \tilde{x}}{\lceil x \rceil_{75} - \lceil x \rceil_{25}}\], \[ z = \frac{x - min(x)}{max(x-min(x))} \], \[ IPQ_i = \frac{A_i}{A_c} = \frac{4 \pi A_i}{P_i^2}\], # % tract population with a Bachelors degree, # Median n. of rooms in the tract's households, # Gini index measuring tract wealth inequality, # Make the axes accessible with single indexing, # Start a loop over all the variables of interest, # Set the axis title to the name of variable being plotted, # Plot unique values choropleth including, # Group data table by cluster label and count observations. However, the variable can still be quite skewed, bimodal, etc. Several of these cells indicate positive linear We can see evidence of this in A Packet made by Mr. Sinn to help you succeed not only on the AP Te. This assignment-update process continues These types of questions are exactly what clustering helps us explore. Typically, in stark contrast to a nucleated settlement, dispersed settlements range from a scattered to an isolated pattern (Figure 12.6). K-means is probably the most widely used approach to areas that are geographically coherent, in addition to having coherent data profiles.

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