Reducing size of labels automatically to fit within polygons

Reducing size of labels automatically to fit within polygons

I want to make labels automatically smaller so they fit within the polygons. The biggest font-size is 12 and I want to decrease the font-size to be restricted to the boundary of the polygons.

There is a time consuming option using the field calculator by using the polygons area field. Is there another way like using a plugin or similar to reduce the font-size automatically?

  • You can make the font size constant in metres rather than points, so it will scale with the map. In Layer Styling, select Metres at Scale instead of Points.

  • You can also make the size in points (or metres) a function of polygon area, using an expression.

    The polygons below are 10000 square km on the outside, and smaller in the middle of the map. The expression for the size (metres at scale) was simple:


    A more complex formula may be useful.

Zoomed in, it scales with the map:

You can base the font size on the area of the polygon.

Not sure if you have already tried this but you can edit the placement of your labels:

Layer properties > Labels > Placement

Select Offset from centroid and choose the whole polygon and the centre quadrant.

Unless you also edit the Scale-based visibility in the Rendering section (Layer properties > Labels > Rendering), the labels will constantly appear at the same size when zooming in or out which may result in labels exceeding the perimeters of the polygon.

How do you choose a font for extremely limited space, i.e. will fit the most READABLE text in the smallest space?

I often have very limited space when creating reports and dashboards for users. I usually use Arial, or Arial Narrow, but UI isn't my area of expertise, so I want to know, how do you determine an optimal font for fitting the most readable text in the smallest space?

Here is an example: Keep in mind this is just an example, as there are many times that space is limited, such as when you need to squeeze a million columns into a report, etc.

Reducing size of labels automatically to fit within polygons - Geographic Information Systems

Worldwide (online), 26th September 2020

Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. However, the customers still face several hurdles with current online shopping solutions. For example, customers often feel overwhelmed with the large selection of the assortment and brands. In addition, there is still a lack of effective suggestions capable of satisfying customers’ style preferences, or size and fit needs, necessary to enable them in their decision-making process. Moreover, in recent years social shopping in fashion has surfaced, thanks to platforms such as Instagram, providing a very interesting opportunity that allows to explore fashion in radically new ways. Such recent developments provides exciting challenges for Recommender Systems and Machine Learning research communities.

This workshop aims to bring together researchers and practitioners in the fashion, recommendations and machine learning domains to discuss open problems in the aforementioned areas. This involves addressing interdisciplinary problems with all of the challenges it entails. Within this workshop we aim to start the conversation among professionals in the fashion and e-commerce industries and recommender systems scientists, and create a new space for collaboration between these communities necessary for tackling these deep problems. To provide rich opportunities to share opinions and experience in such an emerging field, we will accept paper submissions on established and novel ideas, as well as new interactive participation formats.

Keynote Speaker, Ralf Herbrich, Senior Vice President Data Science and Machine Learning at Zalando

Ralf Herbrich leads a diverse range of departments and initiatives that have, at their core, research in the area of artificial intelligence (AI) spanning data science, machine learning and economics in order for Zalando to be the starting point for fashion AI. Ralf’s teams apply and advance the science in many established scientific fields including computer vision, natural language processing, data science and economics. Ralf joined Zalando SE as SVP Data Science and Machine Learning in January 2020.
His research interests include Bayesian inference and decision making, natural language processing, computer vision, learning theory, robotics, distributed systems and programming languages. Ralf is one of the inventors of the Drivatars™ system in the Forza Motorsport series as well as the TrueSkill™ ranking and matchmaking system in Xbox Live.

Keynote Speaker, James Caverlee, Professor at Texas A&M University

James Caverlee is Professor and Lynn '84 and Bill Crane '83 Faculty Fellow at Texas A&M University in the Department of Computer Science and Engineering. His research targets topics from recommender systems, social media, information retrieval, data mining, and emerging networked information systems. His group has been supported by NSF, DARPA, AFOSR, Amazon, Google, among others. Caverlee serves as an associate editor for IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Intelligent Systems, and Social Network Analysis and Mining (SNAM). He was general co-chair of the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020), and has been a senior program committee member of venues like KDD, SIGIR, SDM, WSDM, ICWSM, and CIKM.​

Suggested topics for submissions are (but not limited to):

  • Computer vision in Fashion (image classification, semantic segmentation, object detection.)
  • Deep learning in recommendation systems for Fashion.
  • Learning and application of fashion style (personalized style, implicit and explicit preferences, budget, social behaviour, etc.)
  • Size and Fit recommendations through mining customers implicit and explicit size and fit preferences.
  • Modelling articles and brands size and fit similarity.
  • Usage of ontologies and article metadata in fashion and retail (NLP, social mining, search.)
  • Addressing cold-start problem both for items and users in fashion recommendation.
  • Knowledge transfer in multi-domain fashion recommendation systems.
  • Hybrid recommendations on customers’ history and on-line behavior.
  • Multi- or Cross- domain recommendations (social media and online shops)
  • Privacy preserving techniques for customer’s preferences tracing.
  • Understanding social and psychological factors and impacts of influence on users’ fashion choices (such as Instagram, influencers, etc.)

In order to encourage the reproducibility of research work presented in the workshop, we put together a list of open datasets in the fashionXrecsys website. All submissions present work evaluated in at least one of the described open datasets will be considered for the best paper, best student paper and best demo awards, which will be given by our sponsors.


For the first time, we will offer mentorship opportunities to students who would like to get initial feedback on their work by industry colleagues. We aim to increase the chances of innovative student’s work being published, as well as to foster an early exchange across academia and industry. As a mentee, you should expect at least one round of review of your work p r to the submission deadline. If your work is accepted, you should also expect at least one feedback session regarding your demo, poster or oral presentation.

If you want to participate in the mentorship program, please get in touch via e-mail.

Paper Submission Instructions

  • All submissions and reviews will be handled electronically via EasyChair Papers must be submitted by 23:59, AoE (Anywhere on Earth) on July 29th, 2019.
  • Submissions should be prepared according to the a single-column ACM RecSys format. Long papers should report on substantial contributions of lasting value. The maximum length is 14 pages (excluding references) in the new single-column format. For short papers, the maximum length is 7 pages (excluding references) in the new single-column format.
  • The peer review process is double-blind (i.e. anonymised). This means that all submissions must not include information identifying the authors or their organisation. Specifically, do not include the authors’ names and affiliations, anonymise citations to your previous work and avoid providing any other information that would allow to identify the authors, such as acknowledgments and funding. However, that it is acceptable to explicitly refer in the paper to the companies or organizations that provided datasets, hosted experiments or deployed solutions, if specifically necessary for understanding the work described in the paper.
  • Submitted work should be original. However, technical reports or ArXiv disclosure prior to or simultaneous with the workshop submission, is allowed, provided they are not peer-reviewed. The organizers also encourage authors to make their code and datasets publicly available.
  • Accepted contributions are given either an oral or poster presentation slot at the workshop. At least one author of every accepted contribution must attend the workshop and present their work. Please contact the workshop organization if none of the authors will be able to attend.
  • All accepted papers will be available through the program website. Moreover, we are currently in conversations with Springer in order to publish the workshop papers in a special issue journal.

Additional Submission Instructions for Demos

The description of the demo should be prepared according to the standard double-column ACM SIG proceedings format with a one page limit. The submission should include:

  • An overview of the algorithm or system that is the core of the demo, including citations to any publications that support the work.
  • A discussion of the purpose and the novelty of the demo.
  • A description of the required setup. If the system will feature an installable component (e.g., mobile app) or website for users to use throughout or after the conference, please mention this.
  • A link to a narrated screen capture of your system in action, ideally a video (This section will be removed for the camera-ready version of accepted contributions)

  • Mentorship deadline: June 10th, 2020
  • Submission deadline: July 29th, 2020
  • Review deadline: August 14th, 2020
  • Author notification: August 21st, 2020
  • Camera-ready version deadline: September 4th, 2020
  • Workshop: September 26th, 2020

Selected papers of the workshop have been published in Recommender Systems in Fashion and Retail, by Nima Dokoohaki, Shatha Jaradat, Humberto Jesús Corona Pampín and Reza Shirvany. Part of the Springer's Lecture Notes in Electrical Engineering book series (LNEE, volume 734)

    [presentation] The importance of brand affinity in luxury fashion recommendations, by Diogo Goncalves, Liwei Liu, João Sá, Tiago Otto, Ana Magalhães and Paula Brochado [presentation] Probabilistic Color Modelling of Clothing Items, Mohammed Al-Rawi and Joeran Beel [presentation] User Aesthetics Identification for Fashion Recommendations, by Liwei Liu, Ivo Silva, Pedro Nogueira, Ana Magalhães and Eder Martins
    [presentation] Attention Gets You the Right Size and Fit in Fashion, by Karl Hajjar, Julia Lasserre, Alex Zhao and Reza Shirvany [presentation] Towards User-in-the-Loop Online Fashion Size Recommendation with Low Cognitive Load, by Leonidas Lefakis, Evgenii Koriagin, Julia Lasserre and Reza Shirvany
  • Heidi Woelfle (University of Minnesota, Wearable Technology Lab), Jessica Graves (Sefleuria), Julia Lasserre (Zalando), Paula Brochado (FarFetch), Shatha Jaradat (KTH Royal Institute of Technology)

Shatha Jaradat

KTH Royal Institute of Technology

Nima Dokoohaki

Humberto Corona

Reza Shirvany

The following is a non-exhaustive list of datasets that are relevant for the fashionXrecsys workshop. Participants presenting work in any of these datasets will automatically be part of the workshop's challenge track. If there is a public dataset that you think should be added to the list, please contact the organizing comittee.

Product size recommendation and fit prediction are critical in order to improve customers’ shopping experiences and to reduce product return rates. However, modeling customers’ fit feedback is challenging due to its subtle semantics, arising from the subjective evaluation of products and imbalanced label distribution (most of the feedbacks are "Fit"). These datasets, which are the only fit related datasets available publically at this time, collected from ModCloth and RentTheRunWay could be used to address these challenges to improve the recommendation process.

Description: DeepFashion is a large-scale clothes database which contains over 800,000 diverse fashion images ranging from well-posed shop images to unconstrained consumer photos. DeepFashion is annotated with rich information of clothing items. Each image in this dataset is labeled with 50 categories, 1,000descriptive attributes, bounding box and clothing landmarks. DeepFashion also contains over 300,000 cross-pose/cross-domain image pairs.

Description: DeepFashion2 is a comprehensive fashion dataset. It contains 491K diverse images of 13 popular clothing categories from both commercial shopping stores and consumers. It totally has 801K clothing clothing items, where each item in an image is labeled with scale, occlusion, zoom-in, viewpoint, category, style, bounding box, dense landmarks and per-pixel mask.There are also 873K Commercial-Consumer clothes pairs.

Description: Street2Shop has 20,357 labeled images of clothing worn by people in the real world, and 404,683 images of clothing from shopping websites. The dataset contains 39,479 pairs of exactly matching items worn in street photos and shown in shop images.

Description: Fashionista is a novel dataset to study clothes parsing, containing 158,235 fashion photos with associated text annotations.

Description: The Paper Doll dataset is a large collection of tagged fashion pictures with no manual annotation. It contains over 1 million pictures from with associated metadata tags denoting characteristics such as color, clothing item, or occasion.

Description: Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.

Description: ModaNet is a street fashion images dataset consisting of annotations related to RGB images. ModaNet provides multiple polygon annotations for each image.

Description: The dataset contains over 50K clothing images labeled for fine-grained segmentation.

Description: This is a Women’s Clothing E-Commerce dataset revolving around the reviews written by customers. Its nine supportive features offer a great environment to parse out the text through its multiple dimensions. Because this is real commercial data, it has been anonymized, and references to the company in the review text and body have been replaced with “retailer”.

Description: This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014. This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

Description: In addition to professionally shot high resolution product images, the dataset contains multiple label attributes describing the product which was manually entered while cataloging. The dataset also contains descriptive text that comments on the product characteristics.

Description: The dataset has information of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allows viewing an order from multiple dimensions: from order status, price, payment and freight performance to customer location, product attributes and finally reviews written by customers. The dataset contains real commercial data, it has been anonymised, and references to the companies and partners in the review text have been replaced with the names of Game of Thrones great houses.

Description: This is a pre-crawled dataset, taken as subset of a bigger dataset (more than 5.8 million products) that was created by extracting data from, a leading Indian eCommerce store.

Description: The dataset includes more than 18000 images with meta-data including clothing category, and a manual shape annotation indicating whether the person’s shape is above average or average. The data comprises 181 different users from chictopia. Using our multi-photo method, we estimated the shape of each user. This allowed us to study the relationship between clothing categories and body shape. In particular, we compute the conditional distribution of clothing category conditioned on body shape parameters.


Note that when you resize a plot, text labels stay the same size, even though the size of the plot area changes. This happens because the "width" and "height" of a text element are 0. Obviously, text labels do have height and width, but they are physical units, not data units. For the same reason, stacking and dodging text will not work by default, and axis limits are not automatically expanded to include all text.

geom_text() and geom_label() add labels for each row in the data, even if coordinates x, y are set to single values in the call to geom_label() or geom_text() . To add labels at specified points use annotate() with annotate(geom = "text", . ) or annotate(geom = "label", . ) .

To automatically position non-overlapping text labels see the ggrepel package.


The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving this – a per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing cross-validated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points.

Analyzing large-scale human mobility data: a survey of machine learning methods and applications

Human mobility patterns reflect many aspects of life, from the global spread of infectious diseases to urban planning and daily commute patterns. In recent years, the prevalence of positioning methods and technologies, such as the global positioning system, cellular radio tower geo-positioning, and WiFi positioning systems, has driven efforts to collect human mobility data and to mine patterns of interest within these data in order to promote the development of location-based services and applications. The efforts to mine significant patterns within large-scale, high-dimensional mobility data have solicited use of advanced analysis techniques, usually based on machine learning methods, and therefore, in this paper, we survey and assess different approaches and models that analyze and learn human mobility patterns using mainly machine learning methods. We categorize these approaches and models in a taxonomy based on their positioning characteristics, the scale of analysis, the properties of the modeling approach, and the class of applications they can serve. We find that these applications can be categorized into three classes: user modeling, place modeling, and trajectory modeling, each class with its characteristics. Finally, we analyze the short-term trends and future challenges of human mobility analysis.

This is a preview of subscription content, access via your institution.


Kernel density Edit

Kernel density is a computer based analysis through the usage of geographic information systems employed for the purpose of measuring crime intensity. It takes the map of the area being studied as the basis for analysis then it proceeds to divide the total area or map into smaller grid cells. [1] The size of those grid cells can be selected by the analyst accordingly to the research questions under study or the indented applications. Each cell grid has a center point. Also, it is necessary for the analyst to select a bandwidth. This bandwidth is essentially a search radius from the center of each map grid. When the analysis is run the bandwidth searches the number of crimes reported within each cell. A greater amount crimes located closer to the cell center indicate higher crime intensity. If cells are found to possess high crime intensity rates then they are assigned high values.

Every cell grid in the map is assigned a value. This results in a continues map, a map of a city under the jurisdiction of a given police department for example. This map portraits the crime incidents data or intensity in the form of shades of colors for each grid throughout the area of study. Every part of the map has cells thus every part of the map has intensity value. Therefore, after conducting the kernel density analysis, it can be determine if grid cells with high crime intensity values are clustered together and thus forming a crime hotspot. The cells that possess higher intensity values within the crime hotspots only show the crime density but cannot be further analyze in order to locate the spatial coverage of crime concentrations. The ability to manipulate cell and bandwidth sizes permits analyst to use kernel density for conducting analysis at a small scope level within a crime hotspot.

Hotspot Matrix Edit

The Hotspot Matrix was pioneered by Jerry H. Ratcliffe. [2] It is the analysis of hotspots however, unlike conventional analysis, it is not limited to the examination of hotspots as a mere geographical location. In addition to the implementation of spatial analysis techniques such as kernel density, LISA or STAC it uses an aoristic analysis for which "The basic premise is that if a time of an event is not known, then the start and end time can be used to estimate a probability matrix for each crime event for each hour of the day". [2] Therefore, the hotspot matrix is the combination of both spatial and temporal characteristics pertaining to hotspots in order to determine crime concentration patterns within a high crime intensity area.

Ratcliffe divided the hotspot matrix as having spatial and temporal attributes. The spatial attributes of a hotspot are: Hotpoint referring to a specific place from which a high volume of crimes are generated. The Clustered is geographical characteristic and representation of hotspots where crimes are concentrated with greater density in various areas in the location being studied. Dispersed crimes are those that are distributed across the study region without formulating major clusters of crimes it is the closest form of random distribution of crimes in a hotspot. Ratcliffe also introduced the idea of temporal characteristics of crime. Diffused are hotspots where crimes are likely to happen at any time and there is not a specific window of time for crime incidents. Focused describes a phenomenon where crimes are likely to occur within a hotspot through the day, week, month(s) with greater intensity over a set of small windows of time. Acute pertains to hotspots experiencing the vast majority of incidents in a very small time frame crime incidents outside that time frame are still possible, but nearly nonexistent. These are the six broad categories attributed to the hotspot matrix. These categories can be utilized to identify the areas within administrative boundaries with greater crime intensities. It also facilitates the identification of the type hotspot in the region. After the major crime areas become known, consequently, they can be isolated by the analyst in order to examine them to a closer level. [2]

Empirical study 1 (Chicago) Edit

Loyola Community Safety Project was assembled to investigate the potential relationship between taverns and other local licensed businesses whose primary or partial source of income rely the sales of alcoholic beverages in the area of Roger Park & Edgewater communities in the city of Chicago. This initiative was the result the collaboration of many community groups due to the increasing rates of drug and violent crimes in the region. The researchers had access to the equivalent of a geodatabase, which essentially functions as a big folder with the capabilities of storing multiple files such as aerial photographs or any other file capable of depicting geographical information. This geodatabase was compiled from records of police departments and other community groups it contained data in the form of street addresses of establishments that sell alcohol. This information was stored as software files on a computer this enabled the analysis, the geocoding and the output of the community maps.

The researchers proceeded to compile a list of all businesses in the area of study holding a liquor sales license. The researchers limited themselves from defining Taverns as the source of the crimes. Instead, they included in their study population every business with a liquor license. This facilitated the inclusion of business that do not fit the category of a Tavern in areas with higher poverty levels, but nevertheless serve the same function.

The researchers initiated geocoding which associates an address in the real world to a map – both the addresses of the several types of liquor selling establishments and the crimes that had occurred in places where liquor beverages are sold. The crimes geocoded varied in nature and ranged from disorderly conduct to felonies. After the both crimes and establishments had been geocoded, the maps were overlaid. This facilitated the identification of liquor places with the greater number of crimes within their location or their vicinity.

Some of the limitations in the study were that a high level of coordinates did not match. This was because the raw data was collected by various agencies and for different purposes. The method of analysis was to calculate the hotspot ellipses through the implementation of Spatial and Temporal Analysis of Crime (STAC). Eck and Weisburb (1995) define the process of how STAC works “STAC hot spot area searches begin with individual pin map data and build areas that reflect the actual scatter of events, regardless of arbitrary or predefined boundaries. STAC finds the densest clusters of events in the map and calculates the standard deviational ellipse that best fits each cluster.” (p. 154). It was determined that the number of liquor stores and liquor related businesses were not randomly dispersed in the area. They were generally located in clusters along major roads. This supports the idea that hotspots may contain different arrangements of crime. After the hotspots were identified by the researchers, they continued to examine the hotspots arrangement and took a look at some specific address level crime concentrations. The study found that high concentrations of taverns or liquor stores do not necessarily produce high levels of crime. It concluded that there were places that were responsible for higher levels of crime than others. Therefore, not all crime concentrations are equally generators of crime. Some crime places have environmental cues that facilitate the occurrence and sustainment of crime victimization.

Empirical study 2 (Boston) Edit

This study was designed to reduce youth violence and gun markets in Boston. This was a collaboration of Harvard University researchers, Boston Police Department, probation officers and other city employees that had some level of experience when dealing with young offenders or youth vulnerable to violence. The group initiated a multi-agency study under the perception that a high density of gangs were operating in the area of interest or the city of Boston. It was assumed that youth violence was the direct product of gangs involvement almost in every youth violence incident. Some gang members were interviewed and it was learned that many did not classify themselves as gangs or gang members.

Researchers with the help of gang and patrol officer identify the areas of operation pertaining to each gang or information was also acquired from gang members. Each area was highlighted on a printed map this facilitated the identification of gang-controlled territory. The next step was to go on to hand digitizing the gang territories into a software based map. Through this process, it was discovered that the gang areas of operation were unevenly distributed. Gang territory accounted for less than 10% of Boston.

Data of violent crimes that were confirmed or likely to had been committed by gangs were geocoded and matched with the gang territorial map. This data was obtained from the Boston Police Department for the year of 1994. It is through geocoding and the overlapping the gang territorial map that major concentrations of crime were identified. The ratios of violence incidents were significantly higher under gang areas of operations in contrast to areas free of gang presence. However, not all gangs were equal generators crime or practitioners of the same criminal offenses. Additionally, STAC program was utilize to create hotspot ellipses in order to measure the crime distribution density. It reinforced the previous results that some gangs’ territory experience the higher rates of crime. The crime hotspots located in the regions could then be further analyzed for its unique crime concentration pattern.

Randomized Controlled Trials Edit

The Center For Evidence-Based Crime Policy in George Mason University identifies the following randomized controlled trials of hot spot policing as very rigorous. [5]

Authors Study Intervention Results
Braga, A. A., & Bond, B. J. "Policing crime and disorder hot spots: A randomized, controlled trial", 2008 Standard hot spot policing Declines for disorder calls for service in target hot spots.
Hegarty, T., Williams, L. S., Stanton, S., & Chernoff, W. "Evidence-Based Policing at Work in Smaller Jurisdictions", 2014 Standard hot spot policing Decrease in crimes and calls for service across all hot spots during the trial. No statistically significant difference in crimes found between the visibility and visibility-activity hot spots.
Telep, C. W., Mitchell, R. J., & Weisburd, D. "How Much Time Should the Police Spend at Crime Hot Spots? Answers from a Police Agency Directed Randomized Field Trial in Sacramento, California", 2012 Standard hot spot policing Declines in calls for service and crime incidents in treatment hot spots.
Taylor, B., Koper, C. S., Woods, D. J. "A randomized controlled trial of different policing strategies at hot spots of violent crime.", 2011 Three-arms trial with control, standard hot spot policing and problem-oriented policing group. Problem oriented policing is a policing tactic where the police works in teams that include a crime analyst to target the root causes of crime. Standard hot spot policing was not associated with a significant decline in crime after the intervention. Problem-oriented policing was associated with a drop in “street violence” (non-domestic violence) during the 90 days after the intervention.
Rosenfeld, R., Deckard, M. J., Blackburn, E. "The Effects of Directed Patrol and Self-Initiated Enforcement on Firearm Violence: A Randomized Controlled Study of Hot Spot Policing", 2014 Directed patrol and directed patrol with additional enforcement activity Directed patrol alone had no impact on firearm crimes. Directed patrol with additional enforcement activity led to reduction in non-domestic firearm assaults but no reduction in firearm robberies.
Sherman, L. & Weisburd, D. "General deterrent effects of police patrol in crime "hot spots": a randomized, controlled trial", 1995 Directed patrol Decrease in observed crimes in hot spots.
Groff, E. R., Ratcliffe, J. H., Haberman, C. P., Sorg, E. T., Joyce, N. M., Taylor, R. B. "Does what police do at hot spots matter? The Philadelphia Policing Tactics Experiment", 2014 Four arms trial with control, foot patrol, problem-oriented policing and offender-focused policing groups. Offender-focused policing is a policing tactic where the police targets the most prolific and persistent offenders. Foot patrols or problem-oriented policing did not lead to a significant reduction in violent crime or violent felonies. Offender-oriented policing led to reduction in all violent crime and in violent felonies.
Ratcliffe, J., Taniguchi, T., Groff, E. R., Wood, J. D. "The Philadelphia Foot Patrol Experiment: A randomized controlled trial of police patrol effectiveness in violent crime hotspots", 2011 Foot patrol Significant decrease in crime in hot spots that reach a threshold level of pre-intervention violence.
Weisburd, D., Morris, N., & Ready, J. "Risk-focused policing at places: An experimental evaluation", 2008 Community policing and problem-oriented policing targeting juvenile risk factors No impact on self-reported delinquency.
Braga, A. A., Weisburd, D. L, Waring, E. J., Mazerolle, L. G., Spelman, W., & Gajewski, F. "Problem-oriented policing in violent crime places: A randomized controlled experiment", 1999 Problem-oriented policing-problem places Reductions in violent and property crime, disorder and drug selling.
Buerger, M. E. (ed.) "The crime prevention casebook: Securing high crime locations.", 1994 Problem-oriented policing Unable to get landlords to restrict offender access.
Koper, C., Taylor, B. G., & Woods, D. "A Randomized Test of Initial and Residual Deterrence From Directed Patrols and Use of License Plate Readers at Crime Hot Spots", 2013 License plate recognition software at hot spots Effective in combating auto-theft, the effect lasts 2 weeks after the intervention.
Lum, C., Merola, L., Willis, J., Cave, B. "License plate recognition technology (LPR): Impact evaluation and community assessment", 2010 Use of license plate readers mounted on patrol cars in autotheft hot spot areas No impact on auto crime or crime generally.

There are various methods for the identification and/or establishment of emerging geographical locations experiencing high levels of crime concentrations and hotspots. A commonly used method for this process is the implementation of kernel density this method depicts the probability of an event occurring in criminology it refers to crime incidents. This probability is often measured as a Mean and expressed in the form of density on a surface map. A disadvantage in this approach is that in order to obtain the different degrees of intensity, the map is subdivided into several grid cells. Therefore, the final map output have multiple cells with their own respective crime density degrees which facilitate the comparison between hotspots vs hotspots and places with relative low levels of crime. However, there is not finite line highlighting the begging and the exact end of each hotspot and its respective set or individual crime concentrations. This is assuming that the criminal incidents are not evenly distributed across the space within the hotspot. Also, every grid cell has the same crime density within it therefore, it is difficult to know the exact crime pattern within each cell. One way in which the analysts can handle these set of potential deficiencies is by adjusting the grid cells size on the digital map so they can represent a smaller spatial area on the actual ground. Also, the kernel density map can be overlaid with a dot map for which the crime incidents have been geocoded. This method will enable the analysts to corroborate his/her results by having two analysis of the same area. The kernel density map can be used to identify the spatial area that constitutes the hotspot. After Zooming in to the map, the dot map will enable to identify the individual crime distribution pertaining to each hotspot or even to each cell. Ultimately, this allows for an analysis of blocks, street and specific locations and their spatial relationship to crimes in their surroundings.

A potential deficiency in crime concentration analysis and hotspot identification techniques is that crime analysts generally are limited to analyze data collected from their own law enforcement agency. The collection of this data is limited by administrative and geopolitical lines. Crimes are not contained within social boundaries. These boundaries might restrict the analyst from looking at the entire crime picture. Therefore, by only analyzing within a police department's jurisdiction the researcher might be unable to study the actual or miss the root of the crime concentration due to a partial access of the natural flow of crime that is not restricted by geographical lines.

It is important to know the limitations of each analysis techniques. Thus, it is fundamental to know that some techniques do not include temporal characteristics of crime concentrations or crime incidents. One of the future developments in the analysis of crime concentrations should be the inclusion of time at which the incidents occurred. This will enable to create a hotspot in motion rather than static pictures that only capture one moment in time or portraits all crime incidents as if there exist no difference between the time of each crime's occurrence.

Identification of hotspots and consequently crime concentrations enables law enforcing agencies to allocate their human and financial resources effectively. Detecting areas experiencing abnormally high crime densities provide empirical support to police chiefs or managers for the establishment and justification of policies and counter crime measures. [2] It is through this method of crime analysis that areas with greater rates of victimization within a law enforcement's jurisdiction can received greater amounts of attention and therefore problem solving efforts.

The crime analyst can utilize one of the various spatial analytical techniques for spotting the crime concentration areas. After the spatial extend of these hot areas are defined, it is possible to formulate research questions, apply crime theories and opt the course(s) of action to address the issues being faced therefore, preventing their potential spatial or quantitative proliferation. One example would be asking why a particular area is experiencing high levels of crime and others are not. This could lead the analyst to examine the hotspot at a much deeper level in order to become aware of the hotspot's inner crime incidents placement patterns, randomization or to examine the different clusters of crime. Because not all places are equal crime generators, individual facilities can be further analyzed in order to establish their relationship to other crimes in their spatial proximity. Similarly, every crime concentration analysis is essentially a snapshot of a given number of criminal acts distributed throughout a geographical area. Thus, crime concentrations analyses can be compared throughout different time periods such as specific days of the week, weeks, and dates of the month or seasons. For example, crime snapshots of block Z are compared every Friday over the course of 3 months. Through this comparison, it is determined that 85% of the Fridays during the length of the study block Z experienced abnormally high levels of burglaries in one specific place in Block. Based on this, a Crime prevention through environmental design approach can be taken.

The analyst can then study the specific location and determine the factors that make that facility prone to repeat victimization and a crime facilitator. Also, the analyst could discover that there exist a relationship between the place on block Z and the crime offenders. Or it could be discovered that the place managers or guardians are not fulfilling their duties correctly. [6] Therefore, neglecting the crime target and enabling crime flourishment. It is also possible, that the crime target's physical design and characteristics, plus the nature of the businesses it conducts regularly attract or provide actual and potential offenders in the area some crime opportunities.

In addition, objects taken from the premises as part of the burglaries might be easily accessible or promote low risks of being apprehended. This could be further fortified by or as the application of the crime opportunity theory. All of this is made possible due to identification of hotspot and their respective crime concentrations. Plus the further employment of Ratcliffe's hotspot matrix which depicts the crime concentration patterns within hotspots. Also, his perspective of zooming in to hotspot to examine specific crime generators in order to analyze their spatial and temporal relationship to other crimes in the area of study.


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Label Contour Plot Levels

Create a contour plot and obtain the contour matrix, C , and the contour object, h . Then, label the contour plot.

Label Specific Contour Levels

Label only the contours with contour levels 2 or 6.

Set Contour Label Properties

Set the font size of the labels to 15 points and set the color to red using Name,Value pair arguments.

Set additional properties by reissuing the clabel command. For example, set the font weight to bold and change the color to blue.

Set the font size back to the default size using the 'default' keyword.

Label Contour Plot with Vertical Text

Create a contour plot and return the contour matrix, C . Then, label the contours.

3 Answers 3

For measuring the generalization error, you need to do the latter: a separate PCA for every training set (which would mean doing a separate PCA for every classifier and for every CV fold).

You then apply the same transformation to the test set: i.e. you do not do a separate PCA on the test set! You subtract the mean (and if needed divide by the standard deviation) of the training set, as explained here: Zero-centering the testing set after PCA on the training set. Then you project the data onto the PCs of the training set.

You'll need to define an automatic criterium for the number of PCs to use.
As it is just a first data reduction step before the "actual" classification, using a few too many PCs will likely not hurt the performance. If you have an expectation how many PCs would be good from experience, you can maybe just use that.

You can also test afterwards whether redoing the PCA for every surrogate model was necessary (repeating the analysis with only one PCA model). I think the result of this test is worth reporting.

I once measured the bias of not repeating the PCA, and found that with my spectroscopic classification data, I detected only half of the generalization error rate when not redoing the PCA for every surrogate model.

That being said, you can build an additional PCA model of the whole data set for descriptive (e.g. visualization) purposes. Just make sure you keep the two approaches separate from each other.

I am still finding it difficult to get a feeling of how an initial PCA on the whole dataset would bias the results without seeing the class labels.

But it does see the data. And if the between-class variance is large compared to the within-class variance, between-class variance will influence the PCA projection. Usually the PCA step is done because you need to stabilize the classification. That is, in a situation where additional cases do influence the model.

If between-class variance is small, this bias won't be much, but in that case neither would PCA help for the classification: the PCA projection then cannot help emphasizing the separation between the classes.

The answer to this question depends on your experimental design. PCA can be done on the whole data set so long as you don't need to build your model in advance of knowing the data you are trying to predict. If you have a dataset where you have a bunch of samples some of which are known and some are unknown and you want to predict the unknowns, including the unknowns in the PCA will give you are richer view of data diversity and can help improve the performance of the model. Since PCA is unsupervised, it isn't "peaking" because you can do the same thing to the unknown samples as you can to the known.

If, on the other hand, you have a data set where you have to build the model now and at some point in the future you will get new samples that you have to predict using that prebuilt model, you must do separate PCA in each fold to be sure it will generalize. Since in this case we won't know what the new features might look like and we can't rebuild the model to account for the new features, doing PCA on the testing data would be "peaking". In this case, both the features and the outcomes for the unknown samples are not available when the model would be used in practice, so they should not be available when training the model.

Do the latter, PCA on training set each time

In PCA, we learn the reduced matrix : U which helps us get the projection Z_train = U x X_train

At test time, we use the same U learned from the training phase and then compute the projection Z_test = U x X_test

So, essentially we are projecting the test set onto the reduced feature space obtained during the training.

The underlying assumption, is that the test and train set should come from the same distribution, which explains the method above.

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