geoseries attrib u tes and methods i
play

GeoSeries attrib u tes and methods I VISU AL IZIN G G E OSPATIAL - PowerPoint PPT Presentation

Mar 28, 2024 •432 likes •813 views

GeoSeries attrib u tes and methods I VISU AL IZIN G G E OSPATIAL DATA IN P YTH ON Mar y v an Valkenb u rg Data Science Program Manager , Nash v ille So w are School Shapel y attrib u tes and methods # the geometry column is a GeoSeries

  1. GeoSeries attrib u tes and methods I VISU AL IZIN G G E OSPATIAL DATA IN P YTH ON Mar y v an Valkenb u rg Data Science Program Manager , Nash v ille So �w are School

  2. Shapel y attrib u tes and methods # the geometry column is a GeoSeries type(school_districts.geometry) geopandas.geoseries.GeoSeries GeoSeries.area - ret u rns the area of each geometr y in a GeoSeries GeoSeries.centroid - ret u rns the center point of each geometr y in a GeoSeries GeoSeries.distance(other) - ret u rns the minim u m distance to other VISUALIZING GEOSPATIAL DATA IN PYTHON

  3. GeoSeries . area ret u rns the area of each geometr y in a GeoSeries # area of first polygon in districts print(districts.geometry[0].area) 325.78 VISUALIZING GEOSPATIAL DATA IN PYTHON

  4. School district areas GEOSERIES . AREA # print first 4 rows of school districts and the total number of rows print(school_districts.head(4)) print('There are ', school_districts.shape[0], ' school districts.' ) first_name last_name position district geometry Sharon Gentry Member 1 (POLYGON ((-86.771 36.383...))) Jill Speering Vice-Chair 3 (POLYGON ((-86.753 36.404...))) Jo Ann Brannon Member 2 (POLYGON ((-86.766 36.083...))) Anna Shepherd Chair 4 (POLYGON ((-86.580 36.209...))) There are 9 school districts. VISUALIZING GEOSPATIAL DATA IN PYTHON

  5. # calculate area of each school district district_area = school_districts.geometry.area # print the areas and crs used print(district_area.sort_values(ascending = False)) print(school_districts.crs) 0 0.036641 4 0.023030 8 0.015004 1 0.014205 3 0.014123 5 0.010704 2 0.008328 7 0.007813 6 0.006415 dtype: float64 {'init': 'epsg:4326'} VISUALIZING GEOSPATIAL DATA IN PYTHON

  6. # create a copy of school_districts that uses EPSG:3857 school_districts_3857 = school_districts.to_crs(epsg = 3857) # define a variable for m^2 to km^2 and get area in kilometers squared sqm_to_sqkm = 10**6 district_area_km = school_districts_3857.geometry.area / sqkm_to_sqm print(district_area_km.sort_values(ascending = False)) print(school_districts_3857.crs) 0 563.134380 4 353.232132 8 230.135653 1 218.369949 3 216.871511 5 164.137548 2 127.615396 7 119.742279 6 98.469632 dtype: float64 {'init': 'epsg:3857'} VISUALIZING GEOSPATIAL DATA IN PYTHON

  7. Let ' s Practice ! VISU AL IZIN G G E OSPATIAL DATA IN P YTH ON

  8. GeoSeries attrib u tes and methods II VISU AL IZIN G G E OSPATIAL DATA IN P YTH ON Mar y v an Valkenb u rg Data Science Program Manager , Nash v ille So �w are School

  9. GeoSeries . centroid ret u rns the point at the center of each geometr y in a GeoSeries # centroid of first polygon print(districts.geometry.centroid[0]) Point(-87.256 36.193) VISUALIZING GEOSPATIAL DATA IN PYTHON

  10. School district centroids GEOSERIES.CENTROID # print the first 5 rows of school districts print(school_districts.head()) first_name last_name district geometry Sharon Gentry 1 (POLYGON ((-86.771 36.383... Jill Speering 3 (POLYGON ((-86.753 36.404... Jo Ann Brannon 2 (POLYGON ((-86.766 36.083... Anna Shepherd 4 (POLYGON ((-86.580 36.209... Amy Frogge 9 (POLYGON ((-86.972 36.208... VISUALIZING GEOSPATIAL DATA IN PYTHON

  11. School district centroids # create 'center` column from the centroid school_districts['center'] = school_districts.geometry.centroid # create GeoDataFrame with districts and centers part = ['district', 'center'] school_district_centers = school_districts[part] school_district_centers.head(3) district center 1 POINT (-86.86086595994405 36.2628221811899) 3 POINT (-86.72361421487962 36.28515517790142) 2 POINT (-86.70156420691957 36.03021153030475) VISUALIZING GEOSPATIAL DATA IN PYTHON

  12. GeoSeries . distance () GeoSeries.distance(other) - ret u rns minim u m distance to other # distance from red_pt to centroid cen = districts.geometry.centroid[0] print(red_pt.distance(other = cen)) 24.273 VISUALIZING GEOSPATIAL DATA IN PYTHON

  13. Distance bet w een t w o points GEOSERIES . DISTANCE ( OTHER ) district_one = school_districts.loc[school_districts.district == '1'] district_one.head() first_name last_name district center geometry Sharon Gentry 1 POINT (-86.860 36.262) (POLYGON ((-86.771... VISUALIZING GEOSPATIAL DATA IN PYTHON

  14. Distance bet w een t w o points schools.head(3) name lat lng AZ Kelley Elem 36.021 -86.658 Alex Green Elem 36.252 -86.832 Amqui Elem 36.27 -86.703 # create geometry in schools schools['geometry']=schools.apply(lambda x: Point((x.lng, x.lat)), axis=1) #construct schools GeoDataFrame school_geo=gpd.GeoDataFrame(schools,crs = district_one.crs, geometry = schools.geometry) VISUALIZING GEOSPATIAL DATA IN PYTHON

  15. Distance bet w een t w o points # spatial join schools within dist 1 schools_in_dist1 = gpd.sjoin(schools_geo, district_one, op = 'within') schools_in_dist1.shape (30, 8) VISUALIZING GEOSPATIAL DATA IN PYTHON

  16. # import pprint to format dictionary output import pprint distances = {} for row in schools_in_dist1.iterrows(): vals = row[1] key = vals['name'] ctr = vals['center'] distances[key] = vals['geometry'].distance(ctr) pprint.pprint(distances) {'Alex Green Elementary': 0.030287172719682773, 'Bellshire Elementary': 0.0988045140909651, 'Brick Church College Prep': 0.08961013862715599, 'Buena Vista Elementary': 0.10570511270825833, 'Cockrill Elementary': 0.1077685612196105.... VISUALIZING GEOSPATIAL DATA IN PYTHON

  17. Let ' s Practice ! VISU AL IZIN G G E OSPATIAL DATA IN P YTH ON

  18. Working w ith Foli u m VISU AL IZIN G G E OSPATIAL DATA IN P YTH ON Mar y v an Valkenb u rg Data Science Program Manager , Nash v ille So �w are School

  19. VISUALIZING GEOSPATIAL DATA IN PYTHON

  20. Foli u m p y thon package interacti v e maps b u ilt u pon Lea � et . js VISUALIZING GEOSPATIAL DATA IN PYTHON

  21. foli u m . Map () import folium # construct a map centered at the Eiffel Tower eiffel_tower = folium.Map(location = [48.8583736,2.2922926]) # display the map display(eiffel_tower) VISUALIZING GEOSPATIAL DATA IN PYTHON

  22. VISUALIZING GEOSPATIAL DATA IN PYTHON

  23. Setting the z oom le v el import folium # construct a map centered at the Eiffel Tower eiffel_tower = folium.Map([location = 48.8583736,2.2922926], zoom_start = 12) # display the map display(eiffel_tower) VISUALIZING GEOSPATIAL DATA IN PYTHON

  24. VISUALIZING GEOSPATIAL DATA IN PYTHON

  25. Foli u m location from centroid district_one.head() district center geometry 1 POINT (-86.860 36.262) (POLYGON ((-86.771 36.383... center_point = district_one.center[0] type(center_point) <class 'shapely.geometry.point.Point'> VISUALIZING GEOSPATIAL DATA IN PYTHON

  26. Foli u m location from centroid # reverse the order for folium location array district_center = [center_point.y, center_point.x] # print center point and district_center print(center_point) print(district_center) POINT (-86.86086595994405 36.2628221811899) [36.262822181189904, -86.86086595994405] VISUALIZING GEOSPATIAL DATA IN PYTHON

  27. Adding a pol y gon to a foli u m map # create a folium map centered on district 1 district1_map = folium.Map(location = district_center) # add the outline of district one folium.GeoJson(district_one.geometry).add_to(district1_map) # display the resulting map display(district1_map) VISUALIZING GEOSPATIAL DATA IN PYTHON

  28. VISUALIZING GEOSPATIAL DATA IN PYTHON

  29. Let ' s practice ! VISU AL IZIN G G E OSPATIAL DATA IN P YTH ON

  30. Creating markers and pop u ps in foli u m VISU AL IZIN G G E OSPATIAL DATA IN P YTH ON Mar y Van Valkenb u rg Data Science Program Manager , Nash v ille So �w are School

  31. for row in schools_in_dist1.iterrows(): row_values = row[1] print(row_values) name Alex Green Elementary lat 36.253 lng -86.8322 geometry POINT (-86.8322292 36.2529607) district 1 center POINT (-86.86086595994405 36.2628221811899) Name: 1, dtype: object name Bellshire Elementary lat 36.2697 lng -86.7623 geometry POINT (-86.76230026 36.26968766) district 1 center POINT (-86.86086595994405 36.2628221811899) Name: 8, dtype: object VISUALIZING GEOSPATIAL DATA IN PYTHON

  32. B u ilding marker locations # Construct a folium map for school district 1 district1_map = folium.Map(location = district_center, zoom_start = 11) #create a marker for each school for row in schools_in_dist1.iterrows(): row_values = row[1] location = [row_values['lat'], row_values['lng']] marker = folium.Marker(location = location) marker.add_to(district1_map) display(district1_map) VISUALIZING GEOSPATIAL DATA IN PYTHON

  33. B u ilding marker locations VISUALIZING GEOSPATIAL DATA IN PYTHON

Recommend

I nsura nc e , Critic a l Da ta &amp; Re silie nc e Attrib utio n Sc ie nc e . F o re se e a b

I nsura nc e , Critic a l Da ta &amp; Re silie nc e Attrib utio n Sc ie nc e . F o re se e a b

I nsura nc e , Critic a l Da ta &amp; Re silie nc e Attrib utio n Sc ie nc e . F o re se e a b ility o f L o ss, Ada ptive De sig n &amp; Co st Be ne fit Ana lysis L inde ne E Pa tto n, E sq . E a rth a nd Wa te r L a w, L L C AT

371 views • 11 slides
T he B2 SE RVICE SUIT E www e uda t e u/ se rvic e s www.e uda t.e u/ se rvic e s T his wo

T he B2 SE RVICE SUIT E www e uda t e u/ se rvic e s www.e uda t.e u/ se rvic e s T his wo

T he B2 SE RVICE SUIT E www e uda t e u/ se rvic e s www.e uda t.e u/ se rvic e s T his wo rk is lic e nse d unde r the Cre a tive Co mmo ns CC-BY 4.0 lic e nc e . Co mmo ns CC BY 4 0 lic e nc e Attrib utio n: E UDAT www.e uda t.e

653 views • 30 slides
Generic Methods 36 What are Generic Methods? Generic methods = methods that introduce type

Generic Methods 36 What are Generic Methods? Generic methods = methods that introduce type

Generic Methods 36 What are Generic Methods? Generic methods = methods that introduce type parameters Similar to declaring a generic type but type parameter's scope is limited to method where it is declared The following are

584 views • 6 slides
Clustering ! Hierarchical methods ! Model-based methods ! Density-based methods 1 2 What is

Clustering ! Hierarchical methods ! Model-based methods ! Density-based methods 1 2 What is

Preview ! Introduction Lecture 10 ! Partitioning methods Clustering ! Hierarchical methods ! Model-based methods ! Density-based methods 1 2 What is Clustering? Examples of Clustering Applications ! Cluster: a collection of data objects !

301 views • 8 slides
DESIGN &amp; ANALYSIS METHODS DESIGN &amp; ANALYSIS METHODS FOR DESIGN &amp; ANALYSIS METHODS

DESIGN &amp; ANALYSIS METHODS DESIGN &amp; ANALYSIS METHODS FOR DESIGN &amp; ANALYSIS METHODS

SNAME Philadelphia Section Meeting May 2013 y USA (originally presented at MTU Naval Symposium, September 2011, GERMANY) DESIGN &amp; ANALYSIS METHODS DESIGN &amp; ANALYSIS METHODS FOR DESIGN &amp; ANALYSIS METHODS DESIGN &amp; ANALYSIS

1.11k views • 80 slides
Why mixed methods? Methods should be mixed in a way that has complementary strengths and

Why mixed methods? Methods should be mixed in a way that has complementary strengths and

Mixed, mixing and mul.plying methods Dr Gemma Derrick Centre for Higher Educa.on Research &amp; Evalua.on Why mixed methods? Methods should be mixed in a way that has complementary strengths and non-overlapping weaknesses. It involves the

239 views • 11 slides
Meshless Meshless Methods Meshless Meshless Methods Methods Methods Contents

Meshless Meshless Methods Meshless Meshless Methods Methods Methods Contents

Meshless Meshless Methods Meshless Meshless Methods Methods Methods Contents Introduction Mess Free Methods Element Free Galerkin Method Element Free Galerkin Method Moving Particle Semi-Implicit Method Conclusion C l i

562 views • 38 slides
Monte Carlo Methods An introduction to Monte Carlo (MC) methods How to use MC methods

Monte Carlo Methods An introduction to Monte Carlo (MC) methods How to use MC methods

Numerical and Scientific Computing with Applications David F . Gleich CS 314, Purdue September 12, 2016 In this class: Monte Carlo Methods An introduction to Monte Carlo (MC) methods How to use MC methods Next class to estimate

826 views • 17 slides
Direct Search Methods (nongradient methods) 1. Random search methods 2. Univariate method (one

Direct Search Methods (nongradient methods) 1. Random search methods 2. Univariate method (one

Unconstrained Minimization in R n Direct Search Methods (nongradient methods) 1. Random search methods 2. Univariate method (one variable at a time) 3. Pattern search methods a) Hooke and Jeeves method b) Powells conjugate direction method

154 views • 12 slides
Methods Constructors Instance methods Class methods 30 Constructors have the same

Methods Constructors Instance methods Class methods 30 Constructors have the same

Methods Constructors Instance methods Class methods 30 Constructors have the same name as the class do not return anything they begin with a call to the constructor from the parent class (super(...)) public class MyClass{

563 views • 24 slides
XMLTree Methods 7 January 2019 OSU CSE 1 Methods for XMLTree All the methods for XMLTree are

XMLTree Methods 7 January 2019 OSU CSE 1 Methods for XMLTree All the methods for XMLTree are

XMLTree Methods 7 January 2019 OSU CSE 1 Methods for XMLTree All the methods for XMLTree are instance methods , i.e., you call them as follows: t.methodName(arguments) where t is an initialized variable of type XMLTree 7 January 2019 OSU

1.14k views • 65 slides
CSE CSE 460 460 Evolutionary Evolutionary Methods Methods In this section we will look at

CSE CSE 460 460 Evolutionary Evolutionary Methods Methods In this section we will look at

CSE CSE 460 460 Evolutionary Evolutionary Methods Methods In this section we will look at evolutionary methods Genetic Algorithms Evolution Strategies Genetic Programming Constraints in Evolutionary Methods References

871 views • 57 slides
Benefits of Methods Chapter 5 Methods Write a method once and reuse it anywhere.

Benefits of Methods Chapter 5 Methods Write a method once and reuse it anywhere.

Benefits of Methods Chapter 5 Methods Write a method once and reuse it anywhere. Information hiding: hide the implementation from the user Reduce complexity 1 4 Motivating Example Defining and Using Methods Often we need to

232 views • 12 slides
Roadmap SE process management Waterfall model Incremental methods Agile/XP methods,

Roadmap SE process management Waterfall model Incremental methods Agile/XP methods,

Roadmap SE process management Waterfall model Incremental methods Agile/XP methods, SCRUM Iterative / spiral methods (eg, RUP) Evolutionary methods V -Model CMMI 320312 Software Engineering (P. Baumann) 1 The

339 views • 19 slides
Stochastic Simulation Methods: Variance reduction methods Antithetic variables Bo Friis

Stochastic Simulation Methods: Variance reduction methods Antithetic variables Bo Friis

Variance reduction methods Variance reduction methods To obtain better estimates with the same ressources Exploit analytical knowledge and/or correlation Stochastic Simulation Methods: Variance reduction methods Antithetic

276 views • 4 slides
Methods Mysteries Revealed Wri2ng monolithic programs in the

Methods Mysteries Revealed Wri2ng monolithic programs in the

Methods A method (a.k.a. func2on, procedure, subrou2ne) is a piece of code that performs Methods and Data a useful ac2on. (Savitch, Chapter 5) Up to this

144 views • 10 slides
Carlo (MC) methods Introduction to MC methods Why Scientists like to gamble Monte Carlo Methods

Carlo (MC) methods Introduction to MC methods Why Scientists like to gamble Monte Carlo Methods

Introduction to Monte Carlo (MC) methods Introduction to MC methods Why Scientists like to gamble Monte Carlo Methods 2 Overview Integration by random numbers Why? How? Uncertainty, Sharply peaked distributions Importance

1.22k views • 36 slides
Carlo (MC) methods Introduction to MC methods Why Scientists like to gamble Monte Carlo Methods

Carlo (MC) methods Introduction to MC methods Why Scientists like to gamble Monte Carlo Methods

Introduction to Monte Carlo (MC) methods Introduction to MC methods Why Scientists like to gamble Monte Carlo Methods 2 Overview Integration by random numbers Why? How? Uncertainty, Sharply peaked distributions Importance

785 views • 37 slides
Chapter 5: Monte Carlo Methods Monte Carlo methods are learning methods Experience

Chapter 5: Monte Carlo Methods Monte Carlo methods are learning methods Experience

Chapter 5: Monte Carlo Methods Monte Carlo methods are learning methods Experience values, policy Monte Carlo methods can be used in two ways: ! model-free: No model necessary and still attains optimality ! Simulated: Needs only a

2.17k views • 32 slides
R Regression Methods Interrogate R Output Objects Paul E. Johnson Center for Research Methods

R Regression Methods Interrogate R Output Objects Paul E. Johnson Center for Research Methods

Regression Methods 1 / 72 R Regression Methods Interrogate R Output Objects Paul E. Johnson Center for Research Methods and Data Analysis University of Kansas 2012 Regression Methods 2 / 72 Outline 1 Methods 2 Interrogate Models

844 views • 72 slides
Research Methods E VAN S TRASNICK CS 347 * Your paper is an argument Your methods provide

Research Methods E VAN S TRASNICK CS 347 * Your paper is an argument Your methods provide

Research Methods E VAN S TRASNICK CS 347 * Your paper is an argument Your methods provide evidence Different arguments require different evidence ...and more! From McGrath, Methodology Matters Method triangulation All methods are flawed

576 views • 32 slides
7. Iterative Methods: Roots and Optima Citius, Altius, Fortius! 7. Iterative Methods: Roots and

7. Iterative Methods: Roots and Optima Citius, Altius, Fortius! 7. Iterative Methods: Roots and

Relaxation Methods Minimization Methods Non-Linear Equations Multigrid Applications 7. Iterative Methods: Roots and Optima Citius, Altius, Fortius! 7. Iterative Methods: Roots and Optima Numerical Programming I (for CSE), Hans-Joachim

846 views • 42 slides
Methods for Anomaly Detection: a Survey Kalinichenko L.A., Shanin I., Taraban I. IPI RAN

Methods for Anomaly Detection: a Survey Kalinichenko L.A., Shanin I., Taraban I. IPI RAN

RCDL 2014 Methods for Anomaly Detection: a Survey Kalinichenko L.A., Shanin I., Taraban I. IPI RAN Outline Introduction Motivation Methods taxonomy Metric Data Oriented Methods Distance-based Data Methods

714 views • 34 slides
25. NLP algorithms Overview Local methods Constrained optimization Global methods

25. NLP algorithms Overview Local methods Constrained optimization Global methods

CS/ECE/ISyE 524 Introduction to Optimization Spring 201718 25. NLP algorithms Overview Local methods Constrained optimization Global methods Black-box methods Course wrap-up Laurent Lessard (www.laurentlessard.com)

649 views • 35 slides

More recommend